Due　to　the　conditional　independent　assumption　of　a　CTC　model,　a　language　model　is　usually　added　to　improve　its　speech　recognition　performance.　However,　adding　a　language　model　will　increase　the　complexity　and　computation　cost.　Therefore,　we　proposed　a　simple　and　effective　speech　recognition　method　based　on　CTC　multilayer　loss.　Unlike　the　traditional　CTC　model　which　only　optimizes　the　CTC　loss　of　the　last　layer,　in　this　method,　the　CTC　multilayer　loss,　which　guides　the　training　of　the　model,　is　obtained　by　weighted　summation　of　the　CTC　losses　of　different　layers.　Through　optimizing　the　losses　of　different　layers,　the　information　of　different　layers　of　the　CTC　model　can　be　taken　into　account,　and　the　information　obtained　is　more　comprehensive,　so　that　the　model　obtained　has　better　recognition　performance.　With　a　small　amount　of　code　modification,　this　CTC　multilayer　loss　method　can　well　regulate　the　training　of　CTC　and　improve　the　performance　of　speech　recognition.　Since　this　method　only　changes　the　loss　function　of　the　CTC　model　and　does　not　change　the　structure　of　the　CTC　model　and　its　testing　process,　the　training　stage　is　simple　and　the　testing　stage　has　no　extra　memory　cost　and　computation　cost.　We　evaluated　the　method　on　Aishell-1　dataset　using　WeNet　as　the　baseline,　and　it　was　able　to　reduce　the　character　error　rate　(CER)　by　7.5%　and　improve　speech　recognition　performance　without　adding　a　language　model.　©　2022　ACM.