Analysis of artistic features of ancient Chinese literary works based on multi-scale feature fusion network

Abstract

Abstract Against the background of the rapid development of artificial intelligence technology, the use of artificial intelligence technology to improve the efficiency of artistic feature analysis of ancient Chinese literary works has become a hot topic of current research in literature. In this paper, we propose a multi-scale feature fusion network for the artistic feature analysis of ancient Chinese literary works to address the problems of a single structure and inflexible adaptation of features that appear in RPN networks and path aggregation networks. Then, features are extracted from ancient Chinese literary works, and several adaptive multi-scale feature extraction modules are used to squeeze the incentive and adaptive gating mechanisms for artistic feature extraction and fusion. Finally, the evaluation index system of artistic features of literary works is constructed, and the multi-scale feature fusion network weights the artistic features of ancient Chinese literature. The results show that the average weights of humanistic spiritual features, national patriotic features, literary emotional features and transformative artistic features in ancient Chinese literature and art features are 26.11%, 24.97%, 23.89% and 25.03%, respectively, and the average weight of humanistic spiritual features performs better compared with the other three. This study analyzes the artistic characteristics and cultural values of ancient literary works, which is an effective initiative for modern people to study ancient culture and inherit the national spirit and has important historical significance for developing Chinese literature.