Maps possess prominent advantages in expressing geographic information data in general and in displaying spatial information about events of natural disasters in particular. With the recent enhancement of geographic information acquisition capabilities, the demand for thematic mapping of multitype disaster emergency response has dramatically increased. To solve the problems of insufficient information transmission and lack of combination of features of objects in emergency mapping, we introduce the concept of information entropy, an important indicator of artificial intelligence in the field of maps, and proposes a constructive method for calculating the entropy of comprehensive feature information of map area objects, aimed at the analysis of problems of disaster severity. Through the quantitative description of indicators of the geometry, spatial distribution, and disaster type of an area object, a comprehensive information entropy calculation model of the area object is constructed, through which the comprehensive feature information entropy of the area object is calculated and visualized. The experimental results obtained for typical case analyses show that the comprehensive feature information entropy model is superior to competing methods as it is more suitable for the requirements of human cognition and judgment. Moreover, this model improves the effectiveness of disaster thematic map information transmission and enriches the traditional emergency mapping information transmission theory.