To address the human activity recognition problem and its application in practical situations, a CNN-LSTM hybrid neural network model capable of automatically extracting sensor data features and memorizing temporal activity data is designed and improved by integrating CNN and gated recurrent units as a variant of RNN. A multi-channel spatiotemporal fusion network-based two-person interaction behavior recognition method is proposed for two-person skeletal sequential behavior recognition. Firstly, a viewpoint invariant feature extraction method is used to extract two-player skeleton features, then a two-layer cascaded spatiotemporal fusion network model is designed, and finally, a multi-channel spatiotemporal fusion network is used to learn multiple sets of two-player skeleton features separately to obtain multi-channel fusion features, and the fusion features are used to recognize the interaction behavior, and the weights are shared among the channels. Applying the algorithm in the paper to the UCF101 dataset for experiments, the accuracy of the two-person cross-object experiment can reach 96.42% and the accuracy of the cross-view experiment can reach 97.46%. The method in the paper shows better performance in two-player interaction behavior recognition compared to typical methods in this field.

Taekwondo behavior recognition has become a popular study issue in the past few decades due to its vast range of applications in the visual realm. The research of Taekwondo behavior recognition based on skeleton sequences has received increasing attention in recent years due to the widespread use of depth sensors and the development of real-time skeleton estimate methods based on depth images. In order to characterize the behavioral sequences, the majority of research work currently in existence extracts the spatial domain information of various skeleton joints within frames and the temporal domain information of the skeleton joints between frames. However, this research work ignores the fact that different joints and postures play different roles in determining the behavioral categories. Consequently, this paper presents a spatio-temporal weighted gesture Taekwondo features-based approach for Taekwondo recognition that employs a bilinear classifier to iteratively compute the weights of the static gestures and joint points relative to the action category in order to identify the joint points and gestures with high information content; concurrently, this paper introduces dynamic temporal regularization and Fourier time pyramid algorithms for temporal modeling in order to provide a better temporal analysis of the behavioural features, and ultimately employs support vector machines to complete the behavioural classification. According to experimental results on several datasets, this strategy outperforms certain other methods in terms of recognition accuracy and is highly competitive.

A large amount of course data has been accumulated in the long-term teaching activities of universities. It is of great research value to use the data resources to analyze the course teaching status and provide decision support for improving the course teaching quality. In this paper, we design and implement a course evaluation system based on association rules and cluster analysis, analyze the functional requirements of the course evaluation system, and pre-process the course evaluation data. Students’ performance data are analyzed by FP-growth association rules, and then clustered by K-means, which can improve the accuracy of data evaluation.The evaluation index system of university English teaching quality under the concept of “Thinking and Government” is established. With the results of the sample survey, the main problems of the evaluation method are summarized and analyzed, and corresponding suggestions are put forward, which provide an important reference for promoting the reform of college English course.

Depression is a clinical disease, mainly accompanied by mood or emotional abnormalities, mainly depression, slow thinking, often accompanied by emotional abnormalities, cognitive behavior, psychophysiological and interpersonal changes or disorders. Here, using static and task-state MRI data, we present a comprehensive study of abnormal neural activity in patients with depression through spatiotemporal, static, and dynamic measures, demonstrating its validity as an underlying biological trait. In order to effectively study the role of emotion regulation in depression, a brain dynamic network synthesis method based on support vector machine model and community detection algorithm was established. We selected data on the mental state of 45 patients from a hospital’s psychiatric disease control center. They had no history of hearing impairment and normal (or corrected) vision. All procedures are agreed in writing by each participant. The results show that this method can effectively reduce the depression degree of the subjects, and the multi-level features of the integration of task activation and task regulation connection reach 81% (\(\mathrm{<}\)0.0010, surrogate test) and 83% (\(\mathrm{<}\)0.0016, surrogate test), respectively. The recovery of its depressive psychological state has a significant impact. Numerous studies have used various forms of emotional stimuli to reveal abnormal behaviors and neural responses in multi-channel emotional processing in patients with depression, providing valuable insights into the mechanism of multi-channel emotion regulation in depression.

In order to provide users with better recommendations, it is particularly important to analyze the behavior of users tagging different resources. In this paper, an attention mechanism based on deep learning is designed to effectively capture the features related to the user’s long-term interests and current interests in the session simultaneously, and alleviate the impact of the user’s interest drift that is difficult to deal with by the current session recommendation algorithm on the recommendation accuracy. The main community discovery algorithms are applied to the clustering analysis of the label system, and their performances on different data sets are compared. Besides, we design a personalized recommendation algorithm for the label system. The experimental results show that the proposed algorithm can find the interests of different users and improve the quality of the recommendation system.

Nowadays, people look at a brand not only to see the value of the brand itself, but to understand the cultural value conveyed behind the brand and experience the connotation of the brand culture. Human-computer interaction technology has also gained more application space with the development of the times. Therefore, a psychological model of brand culture based on human-computer interaction was designed in this paper, and a survey of related content was conducted. In terms of user satisfaction survey, it was concluded that the use of the brand culture mental model based on human-computer interaction technology could greatly improve users’ satisfaction with brand culture and make more people love brand culture; in terms of user participation survey, it was concluded that the brand culture mental model designed based on human-computer interaction technology could achieve better profit results at 21:00 on Sunday. Finally, a survey of user stickiness was carried out, and the test results showed that the brand culture mental model based on human-computer interaction technology established the stickiness between users and brands.

Cities are highly concentrated areas of human civilization, the contradiction between urban development and resources and environment has become increasingly prominent. Inefficient use of energy and land resources, shortage of water resources, and environmental pollution are threatening the healthy development of cities. In this paper, the signal reconstruction algorithm and measurement matrix design in the compressed sensing theory are mainly studied. Aiming at the problems of green city environmental monitoring and landscape design, signal underestimation or overestimation caused by the fixed selection step in the iterative process of sparse adaptive matching tracking algorithm, The threshold idea is introduced into atomic selection, and a variable step size strategy is proposed based on the change of step size. The experimental results show that the establishment of the green city environment monitoring and landscape design model system dynamically changes the network topology, so that data can be transmitted in the mobile ad hoc network.

Deep learning is based on scientific educational psychology theory and is an important concept in contemporary learning theory. Therefore, combining in-depth learning with teaching of political courses, to explore teaching strategies of college political courses based on students’ in-depth learning, requirements for implementing new curriculum standards for cultivating students’ core literacy of disciplines, and cultivating students who meet development requirements of times. High-quality talents are of great value and significance. Through questionnaires and sample interviews, this paper focuses on analyzing specific measures for improvement from four aspects: sufficient teaching preparation, effective teaching implementation, scientific teaching evaluation and normalized teaching reflection. It is highly effective and feasible to increase level of students’ deep learning ability to more than 14.65%.

The development of information society requires the reform of traditional English education. The progress of science and technology, especially the progress of Internet information technology, has penetrated into the English teaching system, reconstructed the relationship between the elements of the English teaching system, and provided technical support for the reform of English teaching. These two aspects are the internal and external impetus of English teaching reform. According to the theory of knowledge construction and multimodal information fusion, this research establishes a user-centered knowledge space, which can respond to user needs quickly, emphasizes the integration and integration of multimodal subject knowledge in resource organization, and expresses multi-dimensional relevance in functional form. The experimental results show that the optimized BOPPPS English teaching model is conducive to improving students’ participation in English classroom interaction. In the new information integration technology environment, students are more likely to put forward opinions or suggestions, and the transformation relationship of students’ interactive behavior becomes more complex.

At present, countries all over the world attach great importance to cultural works. These works have become an important engine of economic development and can make good contributions to economic growth. The traditional tracing control scheme of cultural creation works has some problems, such as incomplete information collection, critical point of unit tracing, information fraud, centralized data storage and so on. At the same time, there seems to be a series of problems that can be solved. This paper analyzes the current situation of the review data of cultural creation products, and puts forward the review and analysis scheme of cultural creation works based on large-scale data algorithm and block chain technology. In addition, by combining department chain technology with intranet and traditional database system, an information database about the supply chain of cultural products in crop departments is established. The processing, logistics and sales information and the information of participants are interrelated.