Community　detection　is　an　important　research　direction　in　complex　network　analysis　that　can　help　us　discover　valuable　network　structures.　The　community　detection　algorithms　based　on　multiobjective　particle　swarm　optimization　encode　community　membership　of　nodes　in　particles　and　employ　evolutionary　strategies　to　search　for　the　optimal　community　division.　Existing　algorithms　face　two　challenges:　(1)　they　are　inapplicable　to　large　networks　because　the　evolution　process　is　time-consuming;　(2)　they　are　easy　to　fall　into　local　optima.　In　this　paper,　we　propose　a　novel　algorithm　that　combines　a　label-propagation-based　multiobjective　particle　swarm　optimization　algorithm　with　a　graph　attention　variational　autoencoder　to　realize　community　detection.　On　the　one　hand,　the　label　propagation　strategy　is　involved　in　the　update　of　a　swarm＇s　particles　to　speed　up　its　evolution.　The　optimal　solutions　found　by　the　particle　swarm　optimization　algorithm　are　embedded　into　the　objective　of　the　autoencoder　to　improve　the　embedding　vectors＇　quality.　On　the　other　hand,　the　embedding　vectors　are　used　to　improve　the　solutions　of　the　particle　swarm　optimization　algorithm　to　avoid　its　early　convergence.　The　experiments　on　artificial　and　real-world　networks　demonstrate　the　feasibility　and　effectiveness　of　our　algorithm　compared　with　some　state-of-the-art　algorithms.