Efficient adaptive learning rate for convolutional neural network based on quadratic interpolation egret swarm optimization algorithm

With the development of information technology and network communication, the film and television industry in the new era is rising and growing. In the film and television industry, film and television reviews are an indispensable part of the chain and play an important role in influencing the performance of film and television works to a great extent. In this paper, the ATCNN-SVM model is constructed to obtain sentiment classification results for film and television reviews and improve the generalization ability of the original model. Crawling the film and television reviews on relevant film websites, this text data is subjected to dimensional mining and dimensional correlation analysis, and the overall sentiment tendency of film and television reviews is obtained through sentiment-weighted accumulation. After a series of clustering and fusion steps, seven categories of film elements and five polarity sentiment scores were obtained for each film. When the sentiment score is -13, the frequency of films appearing is at most 2477, and at this time, film reviews mainly focus on filming techniques. Based on the results of the experimental analysis, we summarise the problems of film and television reviews at the present stage, and from a diversified perspective, we make positive and active reviews of domestic and non-domestic films, respectively, and analyse the innovative path of film and television review form evolution.

Research on the Current Situation and Formal Evolution and Innovation Path of Film and Television Criticism Supported by Cloud Platforms

Xie

2024

Knowledge Graph Construction and Personalised Learning Path Generation Mechanism for Online English Teaching and Learning

Ying,

2024

With the accelerated development of educational intelligence, the application of online English teaching has become more and more widespread. This paper constructs a knowledge map for online English teaching, which serves as the foundation for a personalized learning path generation model, with the goal of offering students deeply personalized English teaching services. Spectral convolution is used to transfer the filter and graph signals in the graph convolution network to the Fourier domain for computation, and the graph convolution neural network is used to generate the online English teaching knowledge map. Learner preferences are obtained by improving the knowledge graph convolutional network, and the learning path recommendation algorithm MKGLR is proposed. 50 first-year students of educational technology at Henan Normal University in China have been selected as the research subjects to carry out an online English teaching experiment. After the experiment, the average study duration of students decreased from 83.62 hours to 76.8 hours, the performance of online learning behavior improved significantly, and the number of classroom interactions, the number of homework submissions, and the number of resource accesses increased compared to the pre-experiment (Sig<0.01). There was a significant difference. The evaluation values of the usefulness and satisfaction dimensions increased by 43.42% and 26.77%, respectively, compared to the pre-experiment.

The Effectiveness of Implementing Blended Learning Model in College English Speaking Teaching Supported by Information Technology

Zhang

2024

The blended learning approach has been explored many times in theory and seems to be argued many times about its superiority, but the effect in practice still lacks effective data to prove it. This paper takes full account of the contingencies that exist in teaching practice, focuses on English-speaking teaching as the object of study, and improves the teaching control design. Integrate the concept of blended teaching and clarify the overall mechanism of blended teaching in spoken English. Ensure that the experimental group and the control group have comparable levels of spoken English through random selection, and try our best to exclude the interference of other factors in the teaching experiment. On the other hand, we build an objective model of teaching quality by combining the BP neural network and genetic algorithm. The teaching effect was fully verified by integrating students’ performance and quality evaluation results. The results show that before and after the blended teaching, the students in the control group and the experimental group have different degrees of improvement in satisfaction, learning motivation, and attitude. The model’s evaluation of teaching quality before and after the teaching also validates the effectiveness of the blended teaching mode in teaching spoken English.