Temporal　action　detection　has　gradually　become　a　hot　field　with　the　development　of　video.　This　task　focuses　on　untrimmed　video　applications,　which　is　close　to　practical　applications.　To　improve　the　accuracy　of　temporal　action　detection,　this　paper　proposes　a　novel　framework　based　on　temporal　convolutional　network　with　coarse-grained　clips　construction　and　our　framework　mainly　includes　three　parts　as　follows:　feature　extraction,　coarse-grained　clips　construction　and　improved　boundary　regression　model.　In　particular,　to　make　our　model　more　stable　without　reducing　the　sensitivity　to　boundary,　this　paper　proposes　a　novel　coarse-grained　clips　construction　to　gain　coarse-grained　clips.　Furthermore,　we　refine　action　boundary　with　improved　boundary　regression　model　based　on　temporal　convolutional　network　and　design　three　sub-module　structures　to　preserve　temporal　information.　The　results　on　THUMOS14　illustrate　that　our　framework　could　provide　high　quality　of　action　propose　to　improve　the　detection　accuracy.　©　2019　IEEE.