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.　©　2023　Northeast　University.　All　rights　reserved.