In view of the shortcomings of the sparrow search algorithm in the face of complex problems with strong constraints, non-convexity and non-differentiability, such as unbalanced exploitation and exploration ability, easy to fall into local optimum, premature convergence and low population diversity, a multi-strategy hybrid sparrow search algorithm for complex constrained optimization problems is proposed. Firstly, the opposition-based learning strategy is used to construct a bi-directional initialization mechanism to achieve the purpose of obtaining the initial population with better distribution. Then, a position update formula based on the crossover and mutation operator is designed to expand the search range and enrich the search mechanism for balancing the exploration and exploitation ability of the algorithm, while improving the convergence accuracy and speed of the algorithm. Finally, the community learning strategy is used to refine the population, strengthen the exploitation ability and the ability to jump out of the local optima, and maintain the diversity of the population. The performance of the proposed algorithm is evaluated on 28 real constrained optimization problems of CEC2017 and 1 engineering optimization problems. The experimental results show that the proposed algorithm compared with other optimization algorithms has advantages such as stronger optimization ability, higher convergence accuracy, faster convergence speed and so on, which can be used to effectively solve complex constrained optimization problems.