A new clustering model was proposed to resolve the problem of missing associated information and ambiguous demand change trend in user demand description. The crawler technology was used to obtain user online comments, and the heterogeneous participatory link network was constructed with Jieba word segmentation results. The vocabulary importance was calculated based on the PageRank algorithm, and the PageRank calculation results were used as a basis for filtering to establish a feature vocabulary set. By improving the overlapping community discriminated algorithm, the similarity calculation of subgroup edge attributes was enhanced, and an undirected weighted network of comment feature vocabulary was constructed. By calculating the Jaccard distance, the hierarchical clustering of the network diagram attribute was carried out to determine the user’s purchase decision information. Update the network link with the similarity calculation results of multi-path nodes to achieve the user demand forecasting. Taking a kettle as an example, the improved modularity was 0.69, and the link prediction accuracy rate was 86%. Results show that the proposed clustering model can clarify the potential correlation information of clustering results, and the link prediction results are in line with the objective characteristics of the group effect. The clustering results can assist designers to clarify the needs of user groups in order to target their designs.