In the field of artificial intelligence education, teaching emotion, as the main assessment basis for teaching evaluation, profoundly affects the teaching method, classroom atmosphere and teaching effect of teachers. This thesis proposes a combined network structure, CRNN, by taking advantage of CNN for speech emotion feature extraction and RNN for sequence modeling, and realizes emotion recognition of classroom discourse through DenseNet neural network to realize the crosstalk between each layer and other layers, and LSTM neural network to complete the task of speech emotion classification. On this basis, the open classroom video of the sixth grade of an elementary school is analyzed for sentiment, and the teaching practice of the application of speech emotion recognition model is carried out to study the optimization effect of the model application on the classroom atmosphere of the elementary school. The overall sentiment value of the classroom interaction video floats in the range of 0~1.9, showing a trend of first increasing and then decreasing, reflecting the feasibility of applying the speech emotion recognition model of this paper to classroom sentiment analysis. Through the teaching experiment, the positive emotional performance of the experimental group is more obvious than that of the control group, and 95.46% of the students agree that the application of the model can improve classroom interaction and the overall atmosphere. The speech emotion recognition model studied here can mobilize the classroom atmosphere, and has more important classroom guidance and application significance.

With the continuous development of the rail vehicle business, high-speed rail, locomotive, subway, light rail and other railroad transportation industry to reach the prosperity of the previous scene, the wheelset is an important support and walking parts of the rail train, so the detection of its geometric parameters and tread quality of the safe operation of the vehicle is of great significance. In this paper, based on the principle of binocular measurement vision, the mathematical model of bilinear structured light is used to calculate the three-dimensional coordinates of the spatial points of the wheel pairs of high-speed railways. The collected point cloud data are filtered and smoothed to eliminate the noise contained in the data. Integrate the two point data under the same coordinate system, perform data fusion on the overlapping part to complete the alignment of the point cloud. And extract its eigenvalues to realize the point cloud coordinate transformation. Through testing experiments, the accuracy of high-speed rail wheel pair data measurement and other indicators are studied and analyzed. The measurement accuracy of the journal diameter of the HSR wheelset has a deviation of about 0.003 mm compared with the CMM, meanwhile, the fluctuation range of the HSR wheelset diameter data in the left and right directions is within 0.04 mm and 0.03 mm, respectively, and the stability of the measurement data of the model is good. The point cloud rotation error is between -1.09° and 1.09°, and the first quadrant angle error is between -1.114° and 0.829°, and the model controls the error to be around 1°, and the verification of the pairing accuracy is passed, which can meet the requirements of the production and operation activities.

This paper discusses the application of the neural machine translation model based on language modeling technology in British Victorian literature and its linguistic adaptation. Firstly, the linguistic features of Victorian literary works are analyzed, including thematic content and social background. Then the neural machine translation model based on language modeling technology is designed, and the text style migration method based on style representation is proposed to reproduce the linguistic features of the literary works. The performance of the translation models under the three fusion style methods is compared with five baseline systems, and the BLEU value, style migration accuracy, and style migration fluency of the machine translation model using the text migration decoding module are 37.49, 0.978, and 3.59, respectively, which are all higher than those of other models. Taking the translation of Wuthering Heights as an example, there is not much difference between this model and the human translation in terms of language adaptation evaluation. It shows that the machine translation model designed based on language modeling technology in this paper has better language adaptability for translating Victorian literature.

In today’s deepening education reform, promoting the deep integration of technology and education has facilitated the process of informatization of school education. Vocational education shoulders the important responsibility of cultivating “high-quality laborers and technical talents”, and the reform of informatization of vocational education has gradually become the focus of attention. In this study, we construct a prediction model of learning achievement based on machine learning to optimize the vocational teaching curriculum system. In this paper, before constructing the prediction model, the basic information data and learning behavior data of students are firstly subjected to feature extraction and feature selection. Then CNN combined with BiLSTM and Attention is used to construct the student performance prediction model CNN-BiLSTM-Attention. Finally, based on the performance prediction model, this study proposes the optimization path of the vocational education curriculum system to solve the problem of student employment. The model in this paper achieved the best prediction results in the performance comparison with both the single model and the integrated model, and the indicators were 0.961, 0.953, 0.985, 0.966, and 0.957, respectively. Moreover, it was found that the model had better prediction results in the process of vocational education courses at 80% and above. Among the features, the importance of the relevant features about honor acquisition is higher, all of them are above 0.8, which is an important factor affecting students’ performance. In the actual application of grade prediction, only one student had only 61.6 points in the final semester’s grade prediction, which had the risk of not being able to successfully graduate and proceed to employment. The study shows that the prediction model based on machine learning in this paper has good performance and can provide a strong basis for the reform and optimization of the vocational education curriculum system and promote the informatization process of vocational education.

The application of modern information technology in track and field training has become an important means to improve the training effect. The study analyses the application of smart wearable devices in track and field training, takes the real-time feedback data of smart wearable devices as the index observation point, constructs the evaluation index system of track and field training based on smart wearable devices, and explores the application of factor analysis and fuzzy comprehensive evaluation method. On this basis, teaching experiments are carried out using smart wearable devices and the evaluation system to explore the effect of smart wearable devices on the enhancement of track and field training in athletic performance. The track and field training of the students in the sample colleges and universities was of medium level, with a total score of 73.71, in which the development of students’ will quality and teachers’ grasp of the training situation still need to be improved. After training with smart wearable devices and assessment system, the practicing students got 4.09%~5.01% improvement in standing long jump, 50m run and 800m run, and there was also a significant difference in training interest with the control students (P<0.05). The smart wearable device and evaluation system can achieve real-time data monitoring and training feedback, which can help coaches and students adjust training in time and improve the effect of track and field training.

Writing skills not only promote the learning of other English skills such as listening, speaking and reading, but also effectively promote the internalization of language knowledge, laying the foundation for further improving the development of students’ comprehensive language skills. In this paper, with reference to the application path of information technology in English literacy teaching, we design a SCN-LSTM-based language model, and on this basis, we adopt a bidirectional recurrent network as the language model, and propose an improved SCN-BiLSTM network, which can effectively obtain the contextual relationship of the input sequence. Through the linear interpolation of the language model, the cached language model adaptation is obtained, and the teaching scene corpus is utilized to train the model, and the teaching context-oriented language model adaptation is obtained. Construct ANFIS model to improve the evaluation of English literacy teaching. After the empirical research experiment, the average English reading score of the students in the experimental class after the experiment is 53.631, which is 11.942 points higher than that before the experiment. The writing score is 8.45, which is 0.97 points higher than before the experiment. The application of the adaptive model of English reading and writing based on SCN-LSTM network is very effective.

The era of big data in education has come, data-driven intelligent decision-making has become the development trend in the era of big data, and precise teaching has become the keyword in the era of big data. This paper establishes a real-time dynamic teaching strategy adjustment decision-making model based on the learning characteristics in the process of industry-teaching integration practical training in higher vocational education, and uses Markov decision-making and Q-learning algorithms to solve the optimal teaching strategy in each stage of practical training and learning, which assists the teachers in decision-making and precise intervention. The results of the practical training teaching experiment found that the students in the experimental group, after the dynamic adjustment and intervention strategy implementation of the industry-teaching integration practical teaching, the scores of the practical training theory and application knowledge test were significantly improved (P<0.05), and the students' self-efficacy control sense, sense of effort, and sense of competence were all improved to different degrees. In addition, the scores of depth of understanding (P=0.000) and strategic approach (P=0.000) in practical training learning competencies also increased significantly. The strategy proposed in this study is able to capture the dynamic characteristics of educational data and use the multi-stage dynamic decision-making method to study the development of teaching strategies, which can provide stronger support for accurate teaching decisions and industry-teaching integration of practical training learning.

Prediction of legal decisions using machine learning and artificial intelligence techniques has gradually become an important part of smart court technology. In addition the crime prediction and law recommendation also face the problem of easily confusing crimes. In order to solve these problems, this paper unites multi-task learning models and proposes a model fusion legal verdict prediction model. An attention neural network fusing Transformer Encoder and DPCNN encodes the key semantic information in the case description. The TF-IDF algorithm and TextRank algorithm are applied to extract the keywords of the charge, and the forward propagation network is used as a classifier to constitute a multi-task learning legal verdict prediction model. Using 9 CAIL2018 legal datasets as experimental data, the metrics performance of the multi-task learning legal judgment prediction model proposed in this paper is measured on three subtasks (offense prediction, legal provision prediction, and punishment duration prediction) in LJP. Combining real case information for legal verdict prediction as well as charge differentiation. The verdict prediction results on the CAILBig-Multi dataset show that the mean MP value of the comparison algorithms is 82.925% in the charge prediction. And the MP index of the charge prediction of the multitask learning legal verdict prediction model proposed in this paper is 89.13%, which is significantly higher than the mean value of the comparison algorithms. And the multitask learning model incorporating the keyword information of charges in case analysis can effectively solve the problem of confusing charges.

As the main link of international trade, logistics plays a pivotal role in the entire international trade transactions, and choosing the appropriate logistics path is conducive to cost savings for enterprises. This study combines the traditional logistics model with the actual situation of international trade to select the headway transportation, overseas warehouses and tail distribution as the main elements of enterprise logistics cost optimization in international trade. Based on the cost calculation of the main elements, we design the objective function and constraints of enterprise logistics cost optimization, build the optimization model, and obtain the optimal solution by iterative analysis using the fitness function and genetic operator in genetic algorithm. The empirical analysis shows that after applying the optimization model, the total logistics cost of enterprise D is reduced from US$99,373,500 to US$72,653,400, indicating that the model is effective in optimizing the logistics cost of enterprise D in international trade. This study provides an effective method for the optimization of cross-border enterprise logistics costs, which has a positive role in promoting the development of international trade.

Research on event extraction and constraint encoding of legal cases, using Lawformer as a pre-trained language model for legal sentence prediction model, constructing MJP-Law model to predict the sentence of legal cases. The HAN encoder in the model is utilized to extract the inter-sentence relations in the legal case and construct the relations among the law, the charge, and the sentence period. Compare the performance of this paper’s MJP-Law model with other prediction models on law, charge, and sentence period, and explore the effects of the three subtasks of law, charge, and sentence period on the model through ablation experiments, and compare the prediction effects of a single MJP model and the MJP-Law model on low-frequency charges. In this paper, the MJP-Law model outperforms other prediction models in terms of prediction performance on statute, offense, and sentence. The four models of “MJP-Law”, “MJP-Law_law”, “MJP-Law_SG” and “MJP” had the same prediction performance, which were 95.54%, 89.86%, 89.73% and 89.81%, respectively. “MJP-Law” and “MJP-Law_law”, “MJPLaw_SG” and “MJP” have the same performance in law prediction. After removing the sentencing guidelines and legal sentences, the macro F1 values of the MJP-Law model all showed a decrease.The predictive performance of the MJP-Law model on low-frequency offenses was better than that of the single MJP model.