K-means clustering is sensitive to initial clustering centers and prone to fall into local optimum. In order to solve the problem, a hybrid data clustering algorithm based on an improved krill herd algorithm and K-harmonic means clustering is proposed. Firstly, an improved krill herd algorithm with Lévy flight and crossover operator is proposed to improve stagnating local optimum and low search efficiency of the krill herd algorithm. That is, after each standard krill herd location updating, a new location updating method is added to further search to improve the search ability of the population, at the same time, Lévy flight and crossover operators are used alternately to carry out greedy search for the current population position to enhance the global search ability of the algorithm. The experimental results of 20 benchmark test functions show that the improved algorithm is not easy to fall into the local optimum, which can find the global optimal solution via less times of iteration and ensure the stability of the algorithm. Then, the improved krill herd algorithm and the K-harmonic means clustering algorithm are fused to solve the data clustering problem, that is, the worst individual is replaced by the best individual or the new individual by the K-harmonic means processing the worst individual after each iteration. The test results of five real data sets on UCI show that the fused-clustering algorithm overcomes the defect that K-means is sensitive to the initial clustering center and has stronger global convergence.