As an important window service industry in the city, taxi industry provides all-weather travel mode for people and is an important part of the urban transportation system. Its demand is greatly affected by the internal functions of the city. In order to study the influence of function mixing degree inside the city on taxi travel demand, a spatial information entropy model based on POI data was proposed to measure the function mixing degree inside the city of different Spaces. Then, based on travel OD, the kernel density clustering method was used to analyze the evolution characteristics of the spatial distribution of residents' taxi travel demand in three periods of morning, afternoon and evening peak. After that, GWR was used to study the influence of different space and different time periods on taxi travel demand. Finally, taking the main urban area of xi 'an as an example, and it is divided into 500m×500m grid for empirical study. The research shows that the spatial distribution of the hot spots of taxi travel demand is related to the mixing degree of urban internal functions, and the influence of the mixing degree of urban internal functions on residents' taxi travel demand is different in time and space. This study can provide a reference for discussing the variation of residents' taxi travel demand with the mixing degree of urban internal functions, so as to promote the balance of urban taxi supply and demand, reduce unnecessary no-load, and solve the problems of urban traffic system. Keywords: Urban function mix degree; Taxi travel demand; Effects of time and space

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