With the reform of the education system, the society today raises higher requirements for college teachers, which cause immense psychological stress among them. To enhance the quality of teachers, it is important to analyze the relationship between teaching pressure and self-efficacy. Therefore, this paper tries to analyze and evaluate the relationship between teaching pressure and self-efficacy of college teachers based on artificial neural network. Firstly, a grey correlation analysis (GRA) model was established for the teaching pressure and self-efficacy of college teachers, and the analysis procedure was detailed. Then, the possible multicollinearity of the GRA model was tested. In addition, a linear regression model was established based on Lasso variable selection model and ridge regression variable selection model, aiming to eliminate the multicollinearity between various teaching pressure factors in the GRA model. Finally, a multilabel learning algorithm was proposed based on neural network and label correlation. In this way, the correlations between the various teaching pressure factors and teachers’ self-efficacy were mined automatically. The proposed model proved valid through experiments.

Standard actions are crucial to sports training of athletes and daily exercise of ordinary people. There are two key issues in sports action recognition: the extraction of sports action features, and the classification of sports actions. The existing action recognition algorithms cannot work effectively on sports competitions, which feature high complexity, fine class granularity, and fast action speed. To solve the problem, this paper develops an image recognition method of standard actions in sports videos, which merges local and global features. Firstly, the authors combed through the functions and performance required for the recognition of standard actions of sports, and proposed an attention-based local feature extraction algorithm for the frames of sports match videos. Next, a sampling algorithm was developed based on time-space compression, and a standard sports action recognition algorithm was designed based on time-space feature fusion, with the aim to fuse the time-space features of the standard actions in sports match videos, and to overcome the underfitting problem of direct fusion of time-space features extracted by the attention mechanism. The workflow of these algorithms was explained in details. Experimental results confirm the effectiveness of our approach.

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