Research on Optimization of Modern Literary Works Recommendation Based on Genetic Algorithm

With the advancement of data mining technology, in order to ensure the stickiness of website users, literary works are one of the important manifestations of human civilization, and under the promotion of Internet technology, the way of public acceptance is increasingly dependent on the recommendation of the platform. In order to improve the accuracy of the recommendation algorithm, this study optimizes the text analysis algorithm for reader comments from two aspects: Weighted Information Gain (DWIG) based on intraand inter-class distribution features, and improved genetic algorithm, and Position Distribution Weight Optimization (TF-IDF-DW) of feature terms. In addition, in the research of recommendation algorithm based on community segmentation, reader scoring standard factor and text grade scoring optimization are introduced. Experiments show that the recommendation accuracy of the intelligent recommendation system has been greatly improved, and it is hoped that the results of this study can provide a reference for the application upgrade of the intelligent recommendation system.