To　enhance　communication　efficiency　and　ensure　user　fairness　in　multi-user　multi-channel　communication　scenarios,　based　on　multi-agent　proximal　policy　optimization　(MAPPO)　for　the　application　of　dynamic　spectrum　access　(DSA)　technology,　this　paper　proposes　the　MAPPO-DSA　algorithm.　The　algorithm　addresses　the　issue　of　spectrum　waste　in　single-channel　access　when　multiple　channels　are　simultaneously　idle　by　using　multi-channel　access　as　a　solution.　However,　multi-channel　access　leads　to　an　exponential　increase　in　the　state　and　action　spaces,　resulting　in　high　computational　costs　and　learning　difficulties.　To　tackle　this,　the　paper　introduces　the　MAPPO　deep　reinforcement　learning　(DRL)　algorithm　to　efficiently　learn　and　optimize　access　strategies　in　complex　environments.　The　design　of　MAPPO　incorporates　reinforcement　learning　elements　such　as　observation　and　reward,　as　well　as　shared　network　parameters　to　ensure　user　fairness.　Experimental　results　in　different　scenarios　demonstrate　that　the　proposed　MAPPO-DSA　algorithm　can　learn　near-optimal　access　strategies,　and　approach　the　theoretical　throughput　limit　in　some　scenarios,　outperforming　the　existing　algorithms　significantly　and　effectively　ensuring　user　fairness.　©　2024　Chinese　Institute　of　Electronics.　All　rights　reserved.