AIGC-driven development and innovation of regional education has become an important issue, and in the context of the era when AIGC technology has triggered profound changes in education, the traditional education model is experiencing a paradigm shift from the transmission of knowledge to the cultivation of innovation ability. Based on this, we first construct a model of influencing factors in the application of AIGC in course management based on the rooting theory, and verify the proposed hypotheses to provide a theoretical basis for the construction of course management optimization and multi-level decision-making model. Then we optimize the course management of foreign language teachers in colleges and universities by relying on the all-round and multi-level innovation of AIGC in the field of education, and construct a multi-level decision-making model. In the teaching application practice, the scores of the experimental class on learning interest, learning attitude and learning motivation are all higher than 75 points after practice, and the average score is 8.87 points higher than that of the control class, and the P is less than 0.05. The learning achievement of the experimental class is increased from 73.95 to 80.95 (P < 0.05), and the optimized multilevel decision-making model of this paper has a significant effect on improving students' learning interest, learning attitude, learning motivation and learning achievement, learning attitude, learning motivation as well as learning achievement, which further validates the application effectiveness of the multilevel decision-making model and provides case references for researchers of AIGC-based instructional decision-making.

The research selects the documents related to the legal regulation of civil abuse of rights of action as the research object, crawls the central and local legal regulation database through Python, and uses the social network analysis method to quantitatively analyze the dimensions of the subject of legal regulation from the composition of the subject of legal regulation, the density of the network, the centrality, and the cohesive subgroups, etc. The data preprocessing is carried out on the valid data obtained. Secondly, we pre-processed the acquired valid data, extracted high-frequency words using the improved TF-IDF algorithm, and obtained the probability distribution of the subject strength of “document-subject” and “subject-phrase-item” by calculating the degree of perplexity and utilizing the LDA subject model, and obtained the probability distribution of the subject strength at different stages of civil abuse litigation. In order to obtain the themes and evolution characteristics of the legal regulation of civil abuse of rights of action at different stages, the research results are combined with the results of the study from multiple dimensions. Finally, the research results are combined to design the strategy of legal regulation of civil abuse of rights of action from multiple dimensions.

With the deepening of education modernization, improving teachers’ digital literacy has become the key to promoting the digital transformation of education. The growing demand for professionals in modern society has made the digital literacy of physical education teachers in vocational undergraduate colleges more and more important. This paper defines digital literacy and the digital literacy of vocational undergraduate teachers in turn, explores the four connotations of digital literacy, and proposes strategies to improve the digital literacy of physical education teachers in vocational undergraduate colleges. The entropy value method was used to measure the digital literacy level of physical education teachers in vocational undergraduate colleges, determine the weight of teachers’ digital literacy evaluation indexes, and select and analyze the influencing factors of teachers’ digital literacy. Pearson correlation analysis was conducted on teachers’ digital literacy and influencing factors, as well as various dimensions and influencing factors, and multiple linear regression models were constructed to analyze the improvement path. The measurement results show that in the dimension of digital awareness, the mean values of digital willingness, digital cognition, and digital will are 4.4269, 4.3484, and 4.3748, respectively, indicating that the subject vocational undergraduate physical education teachers are highly willing to learn and use digital technology resources. The correlation coefficients between the dimensions and influencing factors of digital literacy were roughly in the range of 0.4~0.7, and the P values were all < 0.01, indicating that there was a significant positive correlation between them. The path coefficients of "TS→DA", "TE→DA" and "TM→DA" were 0.0533, 0.0796 and 0.0789, which did not reach the significance level, while the other paths reached the significance level (P<0.05), indicating that there was a significant positive impact.

The application of big data in modern enterprise finance is becoming more and more common, and the research adopts the random forest algorithm to explore the enterprise financial risk status, so as to make personalized financial decisions. Construct the enterprise financial risk early warning model based on random forest and construct the financial risk early warning index system. The performance of the random forest model is tested by comparing the financial risk early warning effect of the random forest model with other models. Taking M company as an example, by analyzing its financial risk situation from 2019 to 2023, it puts forward targeted financial decision-making suggestions. The random forest model performs best in the financial risk early warning performance experiment, far outperforming other models. The financial risk status of Company M in 2019-2023 is dangerous, sub-safe, general, dangerous, and general. Although it has been improved in general, it is still in a fluctuating state and the development status is unstable. For the specific financial risk status of Company M, financial decision-making suggestions are proposed for the three aspects of solvency, operating capacity and development capacity.

This paper studies the 3D target modeling method under multi-view video based on deep convolutional network. Through the detailed exposition of the basic theory of 3D target modeling technology and the complete derivation of non-uniform rational B spline curve, this paper establishes technical support such as camera coordinate system for the generation of 3D target model. According to the basic structure of Deep Convolutional Network (DCNN), a DCNN network model suitable for the research scenario of this paper is established, and the model is utilized for feature extraction of images in multi-view videos. The softargmin algorithm is used to generate the parallax map for parallax estimation in the parallax calculation stage. According to the parallax map, voxel-based 3D reconstruction of the target in the multiview video is performed, and the surface reconstruction of the voxel model is performed using the Marching Cubes algorithm, and after obtaining the surface model of the target object, texture mapping is performed to enhance the realism of the model. The deep convolutional network based 3D building method in this paper can effectively realize the feature extraction of target objects in multi-view video. In 3D target modeling, the model in this paper achieves good results on both public and measured datasets, and has obvious performance superiority and generalization ability compared with other methods.

According to the principle, characteristics and use of CCD, this paper designs a laser beam quality measurement program using CCD as a beacon light capture detector and proposes a laser spot detection method based on CCD. The experimental steps and calculation steps for laser beam width measurement and laser power measurement by CCD camera are proposed respectively. The beacon light is used as a light source, and the spot image is processed according to the principle of gray-scale image thresholding to capture the beacon light and present it in the form of a spot on the CCD image sensor. Then, through binarization processing, the spot of the beacon light is distinguished from the background, so as to realize the spot position detection of the beacon light beam. The image data are collected to experimentally detect the laser spot position detection algorithm based on CCD image sensor proposed in this paper, respectively. In the fine-tracking spot position detection, the spot is adjusted in the range of ±9.25mrad, and the solution value is set to be determined every 0.78mrad. The spot center is kept in the range of ±9.05mrad, and centering is carried out every 0.003mrad according to the fine-centering algorithm. The experimental results show that the spots after fine centering are all within the range of ±0.78mrad, and the change trend is consistent with the simulation results, so the laser spot position detection algorithm proposed in this paper is feasible in fine tracking spot position detection.

Driven by the core qualities of the Civics discipline, the requirements of curriculum reform and the needs of teaching practice, the optimization of teaching strategies has become particularly urgent in the field of Civics education. The article introduces the Markov decision-making process and basic elements of reinforcement learning, combines the Q learning algorithm with neural networks, and constructs a deep reinforcement learning model (IDQN) for multiple intelligences with collaborative scheduling. Based on this, a numerical simulation experiment of deep reinforcement learning strategy in Civics teaching was designed and implemented. Through experimental analysis: when the recommended path is 30, the IDQN model has the best learning path recommendation effect, with an IKL of 0.477. The model also has excellent performance in the allocation of teaching resources, with the accuracy, recall and F1 value of 5 tests above 90%. After the numerical simulation of Civic Education teaching, the learning interest, attitude, and motivation of students in the experimental group increased by 27.52% to 34.49%. Under this influence, combined with the learning path and resource allocation provided by the IDQN model, students in the experimental group showed a significant improvement in their learning effect, and the average score of Civic Education Theory was 6.06 points higher than that of the control group.

The continuous development of digital informatization has opened the era of intelligent education in the field of education. Higher education has accumulated a huge amount of data, but it is not fully utilized, and in-depth mining and analysis of these data can reveal the students’ learning and life status and provide powerful support for teaching management. Therefore, the research of using clustering algorithm to build a hierarchical management model for English teaching is very necessary. Clustering algorithm provides an effective way for the analysis of students’ learning behavior, and for the research needs of English teaching, this paper proposes a multi-factor improved K-means clustering algorithm and compares and verifies its clustering effect. For the problem of stratified division of student groups, firstly, the clustering index system of students’ book borrowing behavior and English course learning behavior constructed is used. Then, the improved K-Means clustering algorithm is used to cluster and mine the data of each student’s behavior to discover the student groups under different behaviors, so as to realize the hierarchical clustering of students in hierarchical management. Finally, for English teaching, a student stratification management model is established from three aspects: student stratification, teaching goal stratification and teaching process stratification, which provides important decision support for student stratification determination in English teaching and provides a more rationalized management model for student management workers.

Image alignment is a fundamental problem in the field of computer vision and an important prerequisite for carrying out many other tasks. Firstly, the theoretical basis and realization method of image alignment as well as the process and the method of alignment are introduced to provide alignment ideas. Subsequently, an image alignment method based on the union of multi-scale features is proposed, and a new loss term is introduced to the small-scale features therein, which further improves the distinguishability of the small-scale feature descriptors while guaranteeing the invariance of the large-scale feature descriptor matching therein. Three common alignment algorithms (RIFT algorithm, HAPCG algorithm, and SAR-SIFT algorithm) are selected for stability assessment and quantitative evaluation on the dataset, and an image enhancement algorithm with histogram equalization is used to enhance the dataset. The results show that the feature stability of this paper’s method is described as 99.1%, which is better than other algorithms. Meanwhile the desired effect is achieved on the dataset.

At present, the evaluation of spoken English in domestic universities is affected by the evaluation teachers’ personal cognition, preference, time, energy and other factors, and it is difficult to unify the standard of oral evaluation in the implementation, and the evaluation frequency and timeliness are insufficient to meet the students’ willingness to improve their oral language. In this paper, multimodal speech recognition technology is utilized to firstly collect students’ speech signals through microphone arrays, secondly extract acoustic and linguistic features of speech, and construct multimodal feature vectors by combining visual information such as students’ lip movements and facial expressions. Subsequently, the feature vectors are input into a deep neural network model for training and recognition, fusing LSTM network with attention mechanism to analyze the speech emotion and capture the emotional changes in speech. Meanwhile, the interaction behavior in speech is analyzed by combining temporal convolutional network. Construct a deep reinforcement learning model, introduce a user item interaction layer, design a user interaction simulator, and obtain user feedback on the smart English classroom. Using multimodal speech recognition technology, the temporal waveform of classroom speech is analyzed for sound pressure value, and the normalized sound pressure value range fluctuates around [-1.5,1.5].The average recognition rate of the six emotions rises to 67.86% with the joint effect of LSTM and attention mechanism. By comparing the experiment, analyzing the difference between the experimental class and the control class before and after the reading aloud ability, the average score of the experimental class is 23.945, and the average score of the control class is 21.464, at the same time, the post-test of reading aloud ability corresponding to the experimental class and the control class P=0.005<0.05. It can be seen that the intelligent interactive classroom of English language constructed in this paper has a facilitating effect in the process of teaching reading aloud in the aspect of reading aloud ability of students The classroom can be seen that the intelligent English interactive classroom constructed in this paper has a promoting effect in the process of teaching reading aloud in terms of students' reading ability.