Abstract: To solve the problems of low convergence accuracy, slow convergence speed and local optimum of the original Harris Eagle optimization algorithm, a hybrid strategy improved Harris Eagle optimization algorithm is proposed. Firstly, the good point set is used to initialize the population and increases the population diversity. Secondly, hyperbolic sine-cosine weight factor is introduced to improve the global search ability of the algorithm. Then, the Cauchy mutation operator is introduced in the local search stage to help the algorithm jump out of the local optimal. In addition, the restart strategy is adopted to improve the convergence accuracy and search ability of the algorithm. Through simulation experiments, different types of test functions are used to test the performance of the improved algorithm. Through experimental data and convergence curve analysis algorithm, Wilcoxon rank sum test is used to check the performance of the algorithm. And by solving the pressure vessel design problem, the applicability and effectiveness of the SCCHHO algorithm are further verified. Finally, the improved algorithm is used to optimize the parameters of least squares support vector machine and is applied to Boston housing price prediction. The experimental results further verify the effectiveness of the hybrid strategy improved Harris eagle optimization algorithm.