The big data collection and analysis of public digital culture sharing service is researched in this paper. Big data includes three types of data, namely: ancillary service data, public digital culture sharing service platform operation data and user data. The aim is to build a data analysis platform for the three classes of data. Through the analysis of the three types of data collected, the use of resources and the operation of the platform can be mastered for providing better service for resource organization and scheduling of platform. Through the analysis of three types of the collected data, it can realize all kinds of statistics and analysis services in multidimensional. This paper presents a personalized recommender system of public digital cultural resources.