Abstract: User emotions have a significant impact on spatial attention, spatial decision-making, and spatial memory. Emotions associated with landmarks can improve navigation efficiency while enhancing users' cognitive map abilities. Previous studies focused on the role of emotional landmarks in navigation, but few studies paid attention to landmark extraction methods in complex indoor environments. In this paper, we propose a quantitative model of salience to automate the extraction of emotional landmarks in large shopping malls based on user-generated content (UGC). Firstly, we obtain user comment data of a large shopping mall using web crawler technology. Second, we conduct the sentiment analysis of user comments based on SnowNLP and extend the results to landmark cognitive salience. Then, we combine the Analytic Hierarchy Process (AHP) and Criteria Importance Through Intercriteria Correlation (CRITIC） to calculate the weights of indoor landmark salience indicators and construct a quantitative evaluation model of emotional landmark salience. Finally, we extract hierarchical landmarks using hierarchical clustering algorithms, design multi-scale indoor navigation maps based on hierarchical landmarks to meet user cognition, and verify the usability of the landmark extraction method through user experiments. This work can promote indoor navigation map design standardization and provide a valuable complement to intelligent indoor navigation services.