In order to effectively improve the node coverage of wireless sensor networks, a network coverage optimization algorithm based on hybrid strategy sparrow search algorithm is proposed. Firstly, the Tent chaotic mapping is used to improve the initialization sparrow population and increase the diversity of the population; Reverse learning strategy is used to generate inverse solutions to expand the search range and improve the global search capability; Then the inertia factor is added to select Levy strategy and update the sparrow position to improve the local search ability of the algorithm; Finally the optimal sparrow position is perturbed by random walk strategy to further improve the local search capability. The simulation results show that HSSSA algorithm resulted in a more uniform distribution of nodes and a significant improvement in coverage rate.