This study explores the main influencing factors of college teachers’ ability to teach English reading comprehension through quantitative analysis. In this paper, we designed the scale of “Questionnaire on Teaching Ability of College Teachers’ English Reading Comprehension” and selected the group of M college teachers as the target of the survey. And on the basis of the collected data, using SPSS software, T-test, correlation analysis and multiple linear regression were carried out. The results showed that there was a significant difference (P<0.05) between the teaching effectiveness of teachers in English reading comprehension skills when their education level was below 30 years old or college and below, and that of teachers aged 31 to 40 years old or other highly educated teachers. There is a statistically level difference (P<0.05) between different categories of teachers in both logical reasoning and information processing skills. Teachers' teaching ability passed the significance level test (P < 0.05) with all four independent variables. Their effects on teaching ability are, in descending order: language comprehension ability, information processing ability, logical reasoning ability and cultural comprehension ability, with corresponding regression coefficients of 0.3076, 0.2867, 0.2484 and 0.1225, respectively. It is possible to enhance the college English reading comprehension teaching.

Sentiment analysis belongs to a classification technique with strong practical value, which can identify the hidden imagery in the text. In this paper, we study the sentiment tendency of words in Chinese language and literature texts, design a fuzzy ontology-based method for calculating textual sentiment tendency, build a textual sentiment tendency analysis model (SDNN) based on deep neural network and sentiment attention mechanism, and use the model in Chinese language and literature texts for empirical analysis. At 202 iterations, the Loss value of the SDNN model reaches the lowest value of 0.4626, and the corresponding accuracy, recall, and F1 values are 90.44%, 79.68%, and 84.72%, respectively, and tend to be stable. It indicates that the model has better classification performance and can be used in the task of analyzing the sentiment of Chinese language and literature texts. In addition, this paper explores the rhyme structure of character emotion vocabulary in Chinese language literary discourse, and takes Liu Zongyuan’s related works as an example for empirical analysis of the model. The results show that Liu Zongyuan’s emotion during his ten years in Yongzhou is generally in low, and the proportion of his negative emotion has been above 75%, which is reflected in the text vocabulary emotion is more sad than happy. This paper is of some significance for the study of the emotional tendency of vocabulary in Chinese language literary texts.

Sentiment analysis belongs to a classification technique with strong practical value, which can identify the hidden imagery in the text. In this paper, we study the sentiment tendency of words in Chinese language and literature texts, design a fuzzy ontology-based method for calculating textual sentiment tendency, build a textual sentiment tendency analysis model (SDNN) based on deep neural network and sentiment attention mechanism, and use the model in Chinese language and literature texts for empirical analysis. At 202 iterations, the Loss value of the SDNN model reaches the lowest value of 0.4626, and the corresponding accuracy, recall, and F1 values are 90.44%, 79.68%, and 84.72%, respectively, and tend to be stable. It indicates that the model has better classification performance and can be used in the task of analyzing the sentiment of Chinese language and literature texts. In addition, this paper explores the rhyme structure of character emotion vocabulary in Chinese language literary discourse, and takes Liu Zongyuan’s related works as an example for empirical analysis of the model. The results show that Liu Zongyuan’s emotion during his ten years in Yongzhou is generally in low, and the proportion of his negative emotion has been above 75%, which is reflected in the text vocabulary emotion is more sad than happy. This paper is of some significance for the study of the emotional tendency of vocabulary in Chinese language literary texts.

Sentiment analysis belongs to a classification technique with strong practical value, which can identify the hidden imagery in the text. In this paper, we study the sentiment tendency of words in Chinese language and literature texts, design a fuzzy ontology-based method for calculating textual sentiment tendency, build a textual sentiment tendency analysis model (SDNN) based on deep neural network and sentiment attention mechanism, and use the model in Chinese language and literature texts for empirical analysis. At 202 iterations, the Loss value of the SDNN model reaches the lowest value of 0.4626, and the corresponding accuracy, recall, and F1 values are 90.44%, 79.68%, and 84.72%, respectively, and tend to be stable. It indicates that the model has better classification performance and can be used in the task of analyzing the sentiment of Chinese language and literature texts. In addition, this paper explores the rhyme structure of character emotion vocabulary in Chinese language literary discourse, and takes Liu Zongyuan’s related works as an example for empirical analysis of the model. The results show that Liu Zongyuan’s emotion during his ten years in Yongzhou is generally in low, and the proportion of his negative emotion has been above 75%, which is reflected in the text vocabulary emotion is more sad than happy. This paper is of some significance for the study of the emotional tendency of vocabulary in Chinese language literary texts.

Sentiment analysis belongs to a classification technique with strong practical value, which can identify the hidden imagery in the text. In this paper, we study the sentiment tendency of words in Chinese language and literature texts, design a fuzzy ontology-based method for calculating textual sentiment tendency, build a textual sentiment tendency analysis model (SDNN) based on deep neural network and sentiment attention mechanism, and use the model in Chinese language and literature texts for empirical analysis. At 202 iterations, the Loss value of the SDNN model reaches the lowest value of 0.4626, and the corresponding accuracy, recall, and F1 values are 90.44%, 79.68%, and 84.72%, respectively, and tend to be stable. It indicates that the model has better classification performance and can be used in the task of analyzing the sentiment of Chinese language and literature texts. In addition, this paper explores the rhyme structure of character emotion vocabulary in Chinese language literary discourse, and takes Liu Zongyuan’s related works as an example for empirical analysis of the model. The results show that Liu Zongyuan’s emotion during his ten years in Yongzhou is generally in low, and the proportion of his negative emotion has been above 75%, which is reflected in the text vocabulary emotion is more sad than happy. This paper is of some significance for the study of the emotional tendency of vocabulary in Chinese language literary texts.

Sentiment analysis belongs to a classification technique with strong practical value, which can identify the hidden imagery in the text. In this paper, we study the sentiment tendency of words in Chinese language and literature texts, design a fuzzy ontology-based method for calculating textual sentiment tendency, build a textual sentiment tendency analysis model (SDNN) based on deep neural network and sentiment attention mechanism, and use the model in Chinese language and literature texts for empirical analysis. At 202 iterations, the Loss value of the SDNN model reaches the lowest value of 0.4626, and the corresponding accuracy, recall, and F1 values are 90.44%, 79.68%, and 84.72%, respectively, and tend to be stable. It indicates that the model has better classification performance and can be used in the task of analyzing the sentiment of Chinese language and literature texts. In addition, this paper explores the rhyme structure of character emotion vocabulary in Chinese language literary discourse, and takes Liu Zongyuan’s related works as an example for empirical analysis of the model. The results show that Liu Zongyuan’s emotion during his ten years in Yongzhou is generally in low, and the proportion of his negative emotion has been above 75%, which is reflected in the text vocabulary emotion is more sad than happy. This paper is of some significance for the study of the emotional tendency of vocabulary in Chinese language literary texts.

Sentiment analysis belongs to a classification technique with strong practical value, which can identify the hidden imagery in the text. In this paper, we study the sentiment tendency of words in Chinese language and literature texts, design a fuzzy ontology-based method for calculating textual sentiment tendency, build a textual sentiment tendency analysis model (SDNN) based on deep neural network and sentiment attention mechanism, and use the model in Chinese language and literature texts for empirical analysis. At 202 iterations, the Loss value of the SDNN model reaches the lowest value of 0.4626, and the corresponding accuracy, recall, and F1 values are 90.44%, 79.68%, and 84.72%, respectively, and tend to be stable. It indicates that the model has better classification performance and can be used in the task of analyzing the sentiment of Chinese language and literature texts. In addition, this paper explores the rhyme structure of character emotion vocabulary in Chinese language literary discourse, and takes Liu Zongyuan’s related works as an example for empirical analysis of the model. The results show that Liu Zongyuan’s emotion during his ten years in Yongzhou is generally in low, and the proportion of his negative emotion has been above 75%, which is reflected in the text vocabulary emotion is more sad than happy. This paper is of some significance for the study of the emotional tendency of vocabulary in Chinese language literary texts.

Traditional mechanical manufacturing experimental teaching is limited to one teacher demonstrating operations to several students at the same time, which is difficult to take into account and evaluate the differences in knowledge mastery of different students. In order to improve the above teaching defects, firstly, the teaching evaluation of students’ experimental level is carried out based on their experimental operation behaviors through K-means clustering. On this basis, a deep learning-based knowledge tracking SAFFKT model is designed to empower and update students’ knowledge status. A personalized teaching recommendation method for virtual simulation is proposed based on students’ knowledge state, and the hidden semantic matrix decomposition recommendation algorithm for teaching recommendation is improved and implemented. The AUC and ACC of SAFFKT model are significantly higher than that of the comparison model (p<0.01), and it is robust. The F1 value of the recommended experiments was 0.775, indicating a better recommendation effect. The teaching evaluation model achieves accurate classification of students' experimental behavior and yields different learning characteristics of three types of students. Therefore, the innovative work of virtual simulation teaching strategy in this paper is of practical significance.

In order to solve the enterprise data asset pricing problem in the digital economy environment, this paper utilizes machine learning algorithms such as multiple regression model, BP neural network, and random forest regression, respectively, to price enterprise data assets. Subsequently, the data obtained from each model is fused using the integrated Stacking algorithm to construct an enterprise data asset pricing model with integrated machine learning algorithms. Predictive estimation of the pricing of enterprise data assets is carried out after a detailed justification of the parameter selection of the model. The results show that data capacity, size, quality and freshness are the main influences on data asset pricing. The results of the parameter investigation show that the overall performance of the model is best when the number of node features is 7, at which time the explanatory degree and goodness of fit of the model are 94.33% and 97.27%, respectively. The accuracy, precision, recall and F1 value of the Stacking-based fusion model for enterprise data asset pricing prediction model increased by about 10% compared to the other three models, respectively, to achieve accurate pricing of enterprise data assets.

Teachers’ information literacy is related to the quality and efficiency of education and teaching in higher vocational colleges and universities. In this paper, a dynamic planning-based scheduling method is constructed to improve teachers’ time allocation efficiency and information literacy. First of all, according to the factors and constraints involved in the scheduling problem to determine the goal of solving the scheduling problem, mathematical model, and then the constraints involved in the scheduling of classes, converted into a dynamic planning of the mutually independent and related stages, with 1, 0 indicates whether to meet the constraints. By solving each stage and analyzing the solution of each stage, the optimal value function is summarized, and ACAA is used to traverse all the optimal solutions for each set of constraints. Examples are selected for scheduling test to verify the effectiveness of the algorithm, and the teacher information literacy assessment scale is designed. Applying the class scheduling algorithm to a higher vocational college, the mean value of the overall information literacy scores of the surveyed teachers is 0.15 points higher than the standard reference value, and the effectiveness of the class scheduling algorithm in this paper is verified. Practical experience (58.27%), teaching philosophy (50.19%), and subject requirements (33.36%) are the top three factors affecting teachers’ information literacy.