Traditional industrial research is mostly based on the analytical paradigm of quantitative structure, ignoring patterns of industrial agglomerations. Based on the points-of-interest (POI) data of Gaode Map, this study constructed a firm-cluster-system (FCS) industrial analysis framework combining firm-cluster-system scales. From the perspective of industry classification criteria, it analyzed the geographic nesting hierarchy from micro to macro scales and divided the urban cultural industry POI into five categories: culture and arts industry, press and publishing industry, radio and television industry, sports and fitness industry, entertainment and leisure industry. Using the urban area of Jinan City as the case area, this study integrated nuclear density analysis, industrial landscape ecology model, geographic proximity index, and social network analysis methods to reveal the characteristics of cultural industry POI clustering, industrial cluster landscape, and industrial system network by using the 500 m honeycomb grid. The conclusions of this study are as follows: 1) By establishing a classification system of cultural industries based on POI data and innovatively using the location quotient model to develop research means from industrial POI to industrial landscape, the perspective of cultural industry spatial research has been expanded. 2) Jinan's cultural industry has the characteristics of continuous concentration and scattered aggregation, and there are regional heterogeneity in landscape morphological characteristics. Different industrial types are intertwined to form an ecological network, and there is a coupling relationship at the system level. The entertainment and leisure industry in Jinan has the advantage of group quantity, but the radio and television industry play the role of key species. The findings of this paper provide a basis for industrial regulation and policy optimization at different levels. This research deepens the theory and methods of industrial location analysis and expands the application of geographic big data.