In view of that the sound is a communication medium between product and user, the work aims to propose an interactive visual analysis framework of product sound data to improve the designers' capability of understanding, synthesizing, designing and matching the product sound. Firstly, the sensory description information of designers was fused and integrated with the characteristic parameters of sound through neural network. Secondly, gaussian mixture model was used to describe the product sound data in nonlinear geometric distribution. Finally, the designers input personal prior knowledge and experience to participate in interactive clustering. Based on Anaconda3 of Python, a visual analysis experimental tool for interactive product sound clustering was developed, and the optimal product sound clustering results were obtained. The visual analysis tool for interactive clustering of product sound combines the technical parameters of sound and the auditory reaction mechanism of human brain, allowing users to participate in interaction and integrate their prior knowledge in the clustering process. The parallel view can display the flow direction of data elements and judge the stability of categories in real time. At the same time, visual analysis can help users to compare the similarities and differences of clustering results horizontally, the proportional distribution and rationality of samples, in order to seek the best clustering results.