Wireless communication smart bracelet data include motion data, sleep time data, heart rate and blood pressure data and positioning data, etc. These data have diversity and high complexity, and there are interconnections or interactions between the data, which have high clustering difficulty. To this end, a new data clustering algorithm is studied for wireless communication smart bracelets. The K-medoids algorithm is used to calculate the intra-cluster, inter-cluster, or overall similarity to complete the initial clustering of the bracelet data. Setting the clustering evaluation index can determine the optimal number of clusters. The data objects that are closely surrounded and relatively dispersed are selected as the initial clustering centers and combined with the new index IXB to complete the improvement of the data clustering algorithm. The test results show that the accuracy, recall, and F1 of the research algorithm for clustering the heart rate monitoring dataset, temperature monitoring dataset, energy consumption dataset, and sleep monitoring dataset are higher than 97%, which indicates that the data clustering effect of the algorithm is good.