The K-means clustering algorithm is affected by the initial cluster center, resulting in a low accuracy of the clustering results. The standard flower pollination algorithm (FPA) has slow convergence and low optimization accuracy in the later stage. Therefore, the FPA is improved, and k-means is optimized accordingly. First, a random reverse learning strategy is used to uniformly distribute the population; second, the dynamic transition probability is used to balance the search mode to improve the overall performance of the algorithm; third, the nonlinear inertia weight parameter is introduced into the global search process to improve the global exploration ability; fourth, the optimal individual improves the diversity of the population while decreasing the probability of the algorithm failing. Six standard test functions are used to test the performance of improved flower pollination algorithm (IFPA), and the results show that IFPA is better than FPA in convergence speed and search optimization accuracy. The experimental comparative analysis of k-means cluster optimization based on improved flower pollination algorithm (IFPA-KM) on the University of California Irvine dataset shows that compared with k-means and FPA-KM, IFPA-KM improves the accuracy of clustering and has better stability.