Piano timbre recognition and intelligent synthesis are of great significance in realizing the intelligent teaching of piano timbre. This paper takes the piano timbre teaching based on artificial intelligence interaction as the research object, constructs the timbre expression spectrum based on harmonic structure through the exploration of timbre synthesis, timbre features and other related theories, proposes the timbre feature extraction method based on the time-frequency cepstrum domain of the piano music signal, and then constructs the piano timbre recognition and intelligent synthesis system, realizes the simulation of the piano music, and then provides an intelligent interactive tool for the piano timbre teaching. The method is used to construct a piano tone recognition and intelligent synthesis system. When using the method in this paper, the amplitude of the piano tends to be stable when the frequency is 1600Hz~2400Hz, and there is no noise interference, and when the frequency is 2500Hz and 2800Hz, the amplitude is the lowest, and the recognition performance of the piano timbre is better. Meanwhile, the correct rate of timbre recognition of this method reaches 87.83%, which is better than 58.54% of the comparison method. In addition, the musical tone signals simulated by the method in this paper are very close to the theoretical values of each note of the real piano instrument captured, with an accuracy rate of up to 99%, which proves the accuracy of the simulated piano sounding. And the method can effectively promote the combination of artificial intelligence technology and piano teaching concept, the confidence level of quantitative regression analysis is high, and the evaluation results of teaching quality are good, which provides a reliable theoretical and practical basis for realizing the high-quality teaching of piano timbre.

The value assessment of ancient literary texts and the mining of linguistic features are indispensable parts of academic research and ancient cultural inheritance. This paper uses the multiple regression model as a quantitative analysis tool for value assessment to evaluate the value of ancient literary texts. At the same time, for the linguistic features of ancient literary texts, we put forward the quantitative descriptive definitions of words, phrases, sentences and other multi-layer and multi-latitude, and establish the corresponding calculation formulas. After the assessment of the value of ancient literary texts, it can be learned that, except for the artistic law and the breadth of dissemination, the ancient literary texts are positively correlated with other influencing factors such as the writing method and the rhythm and rhyme, and the gap between the predicted value of the value assessment and the real value is small, with an error of 40% or less in 90% of the cases. In the mining analysis of linguistic features using The Peony Pavilion and The West Wing as research objects, the average word length of the former is slightly higher than that of the latter, while the difference in the distribution of long and short sentences of the latter is relatively large. Meanwhile, the average dependency distance of The Peony Pavilion is 2.42, which is higher than that of The Story of the Western Wing by 0.1, making syntactic analysis more difficult.

Focusing on the learning behavior patterns of students with network behavior, this study mainly adopts sequence cluster analysis and lag sequence analysis to convert learning behaviors into sequences, and constructs a learning behavior pattern recognition model based on network behavior sequences. Aiming at different types of classroom learning behaviors in civic education under the network behavior sequence, a targeted teaching intervention mechanism is designed to help students convert their learning behavior patterns and thus improve their learning effects. In this paper, the online behaviors are clustered into four categories of “integrated, autonomous, compliant, and deviant” according to six level 1 codes, and the correlation coefficients of the online behaviors in the four learning categories range from 0.8539 to 0.9944, which is a very strong correlation. Finally, a survey of the results of the intervention in the classroom of Civic Education found that 75.22% of the students believed that the intervention had improved the learning effect of Civic Education. 67.7% and 77.54% of the students believed that the intervention had improved the enthusiasm and motivation of Civic Education learning. 79.04% of the students were willing to continue to learn independently according to the learning behavior pattern after the intervention.

Physical education teaching resources are an important part of teaching resources, and it is necessary to adopt a sustainable development approach to ensure the rational utilization of resources. In this paper, firstly, the factors affecting the allocation of physical education teaching resources in colleges and universities are analyzed by using principal component analysis and systematic cluster analysis, and the validity of the method is verified. Secondly, it constructs the influential element model of regional physical education teaching resources allocation efficiency level based on Tobit regression, and explores the locational factors affecting the distribution of physical education teaching resources. Finally, relevant countermeasure suggestions were put forward based on the analysis results. Using principal component analysis to downscale the 17 indicators of the influencing elements of physical education teaching resource allocation in the statistical data, four principal components were obtained, whose cumulative contribution rate was as high as 90.22%, which was greater than 85%, i.e., it had a 90.22% degree of explanation for the original data. Then, the dimensionality-decreased data were clustered and realized to evaluate and rank the allocation of physical education teaching resources in 23 sample universities. In addition, the results of Tobit multiple regression analysis showed that factors such as regional geographic location, regional population density, regional economic development and the scale of investment in physical education teaching resources all have different degrees of influence on the allocation efficiency of regional physical education teaching resources.

Existing translation teaching content has certain deficiencies, this paper discusses the computational methods to optimize the translation teaching content by combining the semantic association network model. A domain translation model with joint semantic information is proposed, which constructs a bilingual mapping relation of domain-specific word vectors to obtain the semantic k-nearest neighbors of words in a specific domain,so as to estimate the domain intertranslation degree of words and improve the adaptive ability of the domain translation model. Then a semantic similarity computation model (SRoberta-SelfAtt) incorporating Robert’s pre-training model is proposed. The model incorporates a self-attention mechanism to extract the association of different words within the text, and acquires richer sentence vector information. The proposed domain translation model is able to obtain more accurate translation results while spending less time. Compared with the stability of the iterative process of the basic model, the SRoberta-SelfAtt model has higher iterative stability. The Roberta-based semantic similarity computation model can effectively improve the performance of the word vector model. The experimental results show that the domain translation model with joint semantic information and the SRoberta-SelfAtt model are more practical for the task of optimizing translation teaching content.

Promoting the output and transformation of scientific and technological achievements of higher vocational colleges and universities is not only the topic of promoting the high-quality development of education in higher vocational colleges and universities, but also the way to deeply implement the innovation-driven development strategy. Taking higher vocational colleges and universities in four municipalities directly under the central government as research samples, this study first utilizes the DEA model to measure the transformation efficiency of scientific and technological achievements of higher vocational colleges and universities in four municipalities directly under the central government in the period of 2014-2023, and combines with the literature analysis method to dig out the key influencing factors of their transformation energy efficiency. Then, the fuzzy set qualitative comparative analysis method (fsQCA) is used to carry out empirical research on the transformation efficiency due to inputs and outputs of scientific and technological achievements of the studied higher education institutions and the interactions between their influencing factors, so as to analyze the grouping path of the improvement of the energy efficiency of the transformation of scientific and technological achievements of the higher vocational colleges and universities. In the analysis of the results of measuring the efficiency of the transformation stage of scientific and technological achievements, the efficiency of the transformation stage of scientific and technological achievements of local higher vocational colleges and universities in D city is generally at a high level, with an average value of 0.427. Meanwhile, regional development factors (consistency 0.9081>0.9) and policy factors (consistency 0.9322>0.9) are the necessary conditions for the efficient transformation of scientific and technological achievements of higher vocational colleges and universities, and they are the key influences to improve the energy efficiency of scientific and technological achievements transformation.

Shaanxi folk women’s red has beautiful graphic patterns, which is a treasure of Chinese folk culture. In order to better realize the inheritance and innovation of folk women’s red, this paper refers to the idea of multi-objective optimization, and innovatively designs the composition of ornaments through genetic algorithm and bipartite continuous pattern design method. In order to find out the deep meaning and cultural value of Shaanxi needlework decoration and the unique aesthetic, emotional and life experience of women hidden behind the decoration. In addition, further research on Shaanxi needlework decoration art through multi-objective optimization will not only help to deeply understand the common characteristics of national art, but also help to deeply understand the characteristics of folk art itself. The research shows that the composition scheme designed in this paper has been positively evaluated by experts and consumers, and can promote the inheritance and innovation of Shaanxi folk needlework.

In the long-term teaching practice, various disciplines have accumulated a large number of teaching resources but cannot function fully and efficiently. For this reason, this study constructs a knowledge mapping of college disciplines based on deep learning. First of all, the overall construction of the atlas is planned, the core concepts of the discipline are identified, the relationships between the knowledge points are defined, and the resources corresponding to the knowledge entities and attributes are expanded. Then deep learning is utilized for the entity construction of the subject knowledge graph, the neural network models BiLSTM+CRF and BiLSTM+Attention are used for the subject entity identification and relationship extraction, and finally the subject knowledge fusion and storage is carried out, and the effectiveness of the designed algorithms is verified on the dataset. The data show that the knowledge representation of knowledge graph is conducive to demonstrating the logical meaning between learning materials, facilitating learners to correlate what they have learned previously with what they are learning now, fusing old and new knowledge, and facilitating learners to meaningfully construct knowledge.

In order to realize the intelligent operation and maintenance of electrochemical energy storage power station and make the working process of the power station battery more efficient, stable and safe, this paper establishes a safety monitoring system of electrochemical energy storage power station through multimodal fusion sensing technology. The multi-sensor fusion technology and multi-sensor calibration process are proposed, and the Kalman joint filter fusion algorithm is obtained based on the traditional Kalman filter extension, which fuses the collected multi-modal sensing data to realize the real-time detection of the state information of each battery of the energy storage power station. Simulation experiments are carried out to verify the reliability of the Kalman joint filter fusion algorithm, and the deviation value of this algorithm in the filter fusion processing is only 0.1426, which is lower than that of the comparative sliding average filtering algorithm. The RMSE values of X-axis and Y-axis in the motion target tracking experiments are less than those of the comparative mean drift algorithm 0.189 and 0.1412, and in the speed, they are less than those of 0.0062 and 0.0073, which are better in terms of accuracy performance. And in the application practice of battery safety monitoring system for electrochemical energy storage power station, the error between SOC estimation and actual value is less than 5% in either DST condition or UDDS condition, and the internal resistance 0R change curve is similar to the actual value of the internal resistance, and the estimation error is less than 4%.

The article constructs binocular vision 3D image structure by feature extraction and data acquisition of animated images, setting the base modeling points multi-level, establishing texture mapping modeling relationship, then designing key frame interpolation algorithms such as segmented cubic spline interpolation and quaternionic spherical linear interpolation, and applying geometric algebra to 3D animation modeling, and using a conformal geometric algebra approach to describe the 3D model as well as the dynamic model. Calculation results. The 3D animation modeling using the method of this paper reduces the error of 36.8mm compared with the same type of method, so the effect of using the method of this paper is better than 1other algorithms in 3D human body modeling. In the subjective evaluation of the visual effect of 3D animation video, 19 people think that the video has a strong sense of spatial three-dimensionality, and on the whole, the majority of people think that the animation video developed using the method of this paper is clear, realistic, has a sense of spatial three-dimensionality, smooth movement of the object, and the use of the lens is comfortable, which has a better visual communication effect.