Any　failure　of　the　turbofan　engine,　as　one　of　the　key　components　of　space　shuttle,　can　lead　to　serious　accidents.　Therefore,　it　is　necessary　to　predict　the　remaining　useful　life　(RUL)　to　guarantee　its　reliability　and　safety.　This　article　proposes　a　multitask　learning　(MTL)-based　self-attention　encoding　atrous　convolutional　neural　network　called　MSA-CNN　to　effectively　realize　RUL　prediction.　Specifically,　in　order　to　extract　fault　feature　information,　an　atrous　convolutional　neural　network　(ACNN)　is　used　as　the　auxiliary　task　network,　which　is　more　efficient　than　the　traditional　convolutional　neural　network　(CNN)　in　the　process　of　down　sampling.　Moreover,　a　model　with　ACNN　and　self-attention　encoder　(SAE)　is　used　as　the　main　task　network　to　capture　short-long　term　dependencies　in　a　time　sequence　and　thus　realize　RUL　prediction.　Compared　with　other　recurrent　neural　networks　(RNNs),　SAE　proposed　in　this　article　has　the　advantage　of　parallel　computation.　Besides,　a　novel　multitasking　loss　function　is　also　proposed　to　realize　the　interaction　among　multiple　tasks.　After　MSA-CNN　experiments　on　four　subsets　of　commercial　modular　aero　propulsion　system　simulation　(C-MAPSS)　dataset,　the　root　mean　square　error　(RMSE)　average　between　the　predicted　RUL　and　the　real　value　is　about　14.66,　which　is　better　than　the　existing　methods.　Several　other　comparative　experiments　were　conducted　to　verify　the　benefits　of　each　submodule.