Aiming　at　the　navigation　problem　of　mobile　robots　indoor　environment,　the　traditional　navigation　algorithm　based　on　D3QN　has　some　problems　such　as　sparse　reward　and　slow　training　speed　of　the　neural　network.　This　paper　proposes　a　deep　reinforcement　learning　Algorithm　(LN-D3QN)　based　on　the　D3QN　network　to　realize　collision-free　autonomous　navigation　of　mobile　robots.　To　improve　the　efficiency　of　mobile　robot　learning　and　exploration,　the　Vision　Sensor　is　used　to　acquire　the　input　data　from　the　environment,　and　the　layer　normalization　method　is　used　to　normalize　the　input　data.　An　improved　reward　function　is　designed,　which　improves　the　reward　value　of　the　algorithm,　optimizes　the　state　space,　and　alleviates　the　problem　of　sparse　reward　to　some　extent.　The　data　is　stored　in　a　priority　experience　replay　pool,　and　the　network　is　trained　using　small　batches　of　data.　In　addition,　we　evaluate　our　method　by　experiment　related　to　indoor　navigation.　The　experiments　show　that　the　robot　trained　by　the　improved　D3QN　algorithm　can　adapt　to　the　unknown　environment　more　quickly　than　the　basic　D3QN　algorithm.　The　network＇s　convergence　speed　is　also　improved,　and　it　can　complete　the　obstacle　avoidance　navigation　task　more　efficiently.　©　2022　IEEE.