Aiming　at　the　high　dimension,　slow　convergence　and　complex　modelling　of　traditional　path　planning　algorithms　for　mobile　robots,　a　new　intelligent　path　planning　algorithm　is　proposed,　which　is　based　on　deep　reinforcement　learning　soft　actor-critic(SAC)　algorithm　to　save　the　poor　performance　of　robot　in　complicated　environments　with　static　and　dynamic　obstacles.　An　improved　reward　function　is　designed　to　enable　mobile　robots　to　quickly　avoid　obstacles　and　reach　targets　by　using　state　dynamic　normalization　and　priority　experience　pool　techniques.　To　evaluate　the　performance,　a　pygame-based　simulation　environment　is　constructed.　Compared　with　proximal　policy　optimization(PPO)　algorithm,　experimental　results　show　that　the　cumulative　reward　of　the　proposed　method　is　much　higher　than　that　of　PPO,　and　the　more　robust　than　PPO.　©　2023　Acta　Simulata　Systematica　Sinica.　All　rights　reserved.