Female Characterization and Cultural Characterization of Water Margin Based on Cognitive Mapping Constructs

Abstract

Abstract The Water Margin portrays many typical female images, and different female character images make the characters more fleshed out so that the readers will never forget them. In this paper, we construct cognitive mapping based on the cognitive mapping of female characters in Water Margin. In order to more intuitively display female characters and their embedded cultural characteristics, we choose a multilayer perceptual machine to learn the representation of emotion score vectors and combine the temporal and semantic relationships in the text with long and short-term memory networks to construct an emotion dictionary of female characters. GAN is utilized in female characterization to provide a foundation for thoroughly analyzing the cultural characteristics of female character roles. The method given in this paper was validated and analyzed in terms of female characterization and cultural characteristics. The results show that on the EmotionROI dataset, the classification accuracy of the emotion lexicon is 9.34% higher than that of the GOSR algorithm, and the classification accuracy of various emotion categories reaches more than 70%. The image generation quality of the GAN method is 63.96% on the BLUE-4 dataset, and the final CIDER index score on the MSVD dataset tends to be 1.15%. The image of female characters in Water Margin mainly conveys a variety of different images such as heroic, ugly and complex, which represents the real state of women at the bottom of society in the feudal period of the late Yuan and early Ming Dynasties, and provides a certain reference for understanding the cultural traits under the feudal society.