The　axial　power　deviation　of　a　reactor　can　reflect　the　axial　power　distribution　of　the　core　and　the　operation　of　the　reactor.　Aiming　at　the　difficulties　in　predicting　the　axial　power　deviation　under　variable　operating　conditions,　this　paper　proposes　a　prediction　method　of　reactor　axial　power　deviation　based　on　the　combined　feature　selection　and　temporal　convolutional　network　(TCN).　Taking　the　basic　principle　of　axial　power　deviation　control　as　the　starting　point,　this　paper　analyzes　the　factors　affecting　the　change　of　axial　power　deviation,　comprehensively　analyzes　the　redundancy　and　correlation　among　multi-dimensional　features,　uses　the　combined　feature　selection　strategy　to　form　the　optimal　feature　subset　for　axial　power　deviation　prediction,　constructs　the　key　correlation　feature　data　for　axial　power　deviation　prediction,　and　inputs　it　into　TCN　to　capture　dynamic　causality,　so　as　to　achieve　the　prediction　of　reactor　axial　power　deviation.　Experimental　studies　show　that　the　proposed　method　can　deeply　explore　the　temporal　causal　change　characteristics　of　the　parameters　related　to　the　axial　power　deviation　of　the　reactor,　accurately　predict　the　development　trend　of　the　axial　power　deviation,　solve　the　problem　that　the　traditional　prediction　model　does　not　predict　and　track　in　time　under　complex　operating　conditions,　and　provide　an　auxiliary　reference　basis　for　the　reactor　status　monitoring　and　safe　operation　of　nuclear　power　plants.　©　2025　Atomic　Energy　Press.　All　rights　reserved.