The rapid development of 5G communication technology has led to a comprehensive enhancement of the industrial Internet of Things (IIoT). The scale of IIoT data will become larger, and the dimensionality of data will become higher. As a result, how to efficiently use stream clustering for IIoT data mining is an urgent problem. In this regard, this paper proposes an adaptive clustering method of an IIoT data stream. The algorithm exploits the high density between micro-clusters and calculates the local density peaks of each micro-cluster node to adaptively generate the number of macro-clusters. It employs a gravitational energy function to recursively update the micro-clusters on line and removes the computation between edge-intersecting micro-clusters to achieve a reduction in the computational effort required to maintain the macro-clusters. The theoretical analysis and experimental comparison show that the proposed method has higher-quality clustering results than the current mainstream stream clustering algorithms.