Analyzing spatial agglomeration in creative industries is important for urban-based spatial construction and regional innovative growth. However, obtaining a method that predicts creative clusters dynamically and visually is difficult. This study proposes a visual prediction method for dynamic clustering image used in creative industries. First, a clustering index and complete spatial clustering model is selected based on the location entropy index and Ellison–Glaeser index. Then, space offset is obtained using points of interest-spatial dynamic clustering algorithm to compute the weight iteration using urban regional coordinate points and specified rules. Finally, e-charts are used to realize output from a creative clustering simulation model for computer imaging. Using Canvas Renderer’s 3D simulation, ArcGIS PRO’s geographic information, and E-charts’ dynamic visual component, a spatial agglomeration image of a large amount of data from creative industries with browser functions is formulized. This article takes the creative industry zone in Shanghai as the research object, and the research results show that: ① Based on comprehensive clustering algorithms, 7 creative spatial clusters in Shanghai’s industrial areas can be quickly and effectively identified, and 3 sets of Bubble set preliminary trajectory views, 3 sets of Canvas dynamic simulation time series views, and E-charts spatial dynamic local views can be formed. ② The detection of spatial landmarks within the creative industry area showed that the agglomeration degree accounted for approximately 30% of the checkpoint inspection measurement, with an average movement trajectory of 4.88km, a regional agglomeration measurement value of 0.84, and a dynamic agglomeration evaluation index of 5.01, confirming the preliminary effectiveness of the method From the perspective of spatial agglomeration, Putuo District, Xuhui District, and Pudong District exhibit obvious spatial agglomeration distribution characteristics, manifested as high-value clustering, insignificant clustering, and low value clustering. The number of high-value clusters in industrial space increases, while there are fewer areas with low value clustering and no statistical significance Summarize the spatial agglomeration of urban creative industries and propose control response strategies such as sharing, uniform distribution, and siphoning. Enriched the field of urban geographic information visualization technology.