Data Mining and User Profile Construction in Marketing Strategy of Cultural Industry

Abstract The rapid development and popularization of Internet technology brings opportunities, and the role of data mining technology in cultural industry marketing becomes more and more prominent, and it is also of strategic significance to formulate the marketing strategy of cultural industry through user profile. This paper uses data mining technology to clean, integrate and standardize user information data in cultural industry marketing. On this basis, a user portrait model based on the firefly K-means algorithm is proposed, and the firefly algorithm is used to find the optimal solution as the initial clustering center of the K-means algorithm to realize the establishment of the cultural industry marketing customer portrait. The cultural and creative products industry of public libraries is selected as the research object, and 15,068 effective user data of public library A in the past five years are used as samples to carry out user profile clustering analysis. The potential main customers are “leisure and recreation” users, with a sample size of 9,995, accounting for 66.33%. The proportion of “participation-experience type” users and “socialite type” users is 12.34% and 19.07%, which are potential secondary customers. The lowest percentage of potential marginal customers is the “target learning” users, only standing at 2.26%.