Analysis of the urban creative industry' s image visualization based on the perspective of human geography is of great significance for the integration between urban deep space and regional innovation development. However, the intelligent dynamic spatiotemporal modeling of Swarm groups is insufficient in meeting the visual development of the spatial clustering of creative industries. This study aims to provide a basis for decision-making within city management. Starting from the influencing factors of spatial clustering of creative industries in urban areas, a novel process of density-based interest spatial clustering of path (DBICP) is proposed together with a computer browser to aggregate visual images. First, according to the indicator system of influencing factors, and through the space bayonet traffic data and industry indicator data during 2014 to 2018, preprocessing is performed for constructing a spatial standard clustering algorithm: The density-based spatial clustering of applications with noise. Second, a hierarchical optimization of clustering density is performed to develop a new DBICP algorithm and obtain a preliminary trajectory image. Finally, using source code translation, the output of spatially-aggregated trajectory images under the browser interface is completed. Through the selection of 7 creative spatial indicators, the selection of more than 4000 points of interest, 2 groups algorithm tests, data of 3 groups bubble-set preliminary planning, 3 sets of Canvas dynamic simulation sequential planning, and E-charts spatial dynamic partial planning are accomplished. The average moving trajectory distance is 4.88 km, the regional agglomeration degree is 0.84, and the dynamic agglomeration evaluation index is 5.01. The results of the process as applied to the sample city of Shanghai show that three different clustering patterns have been formed in the spatial distribution of creative industries in Putuo District, Pudong New District, and Xuhui District, thus evidencing the control response strategy of allocation, uniform distribution, and siphon. The vector clustering image generated by the method proposed in this paper can explore the clustering characteristics of smart dynamic activities of the urban big data in the future and can also effectively solve practical urban problems, such as business clustering graphical measurement and community traffic image survey, and provide relevant technical support and research means for the large-scale spatial dynamic clustering supervision of urban geography. The method overcomes the lack of clustering classification and trajectory measurement in traditional images, effectively finding clustering information of image trajectories from the index data. This in turn embodies the interdisciplinary integration of geographic and sociological information, thus providing a clustering method.