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To improve the clustering of the clustering algorithm, the bionic optimization algorithm and the kmeans clustering algorithm are combined to realize data clustering (BFOA-K). In the clustering process, the fruit fly optimization algorithm is used to determine the centroid of the k-means clustering algorithm, and the k-means clustering algorithm is used for data clustering. To solve the problem that k-means is sensitive to the initial centroid and easy to fall into the local optimum, and the influence of the fruit fly optimization algorithm on the flight step length, the F distribution is used to dynamically change the step length which improves the global search ability of the algorithm. The elite retention strategy is adopted to increase the diversity of the fruit fly population, expand the scope of the search and improve the search efficiency. Four UCI standard datasets are used to test the algorithm. The experiment shows that the algorithm is better than other comparison algorithms in all clustering evaluation indicators. The BFOA-K improves the convergence of the algorithm with effectiveness and feasibility. [ABSTRACT FROM AUTHOR]