The　combination　of　deep　learning　and　intrusion　detection　has　become　a　hot　topic　in　cyberspace　security.　In　unstable　network　security　situation,　how　to　accurately　detect　abnormal　traffic　is　an　important　task　for　intrusion　detection.　Each　sample　in　the　intrusion　data　contains　multiple　features,　but　not　every　feature　can　determine　the　final　nature　of　the　sample.　Some　features　will　even　affect　the　judgment　ability　of　the　model.　To　solve　this　problem,　an　intrusion　detection　model　based　on　residuals　of　a　double　routing　deep　capsule　network　is　proposed.　The　model　uses　a　deep　capsule　network　to　enhance　the　identification　and　extraction　of　features,　which　can　extract　higher　dimensional　data　features.　A　hybrid　attention　mechanism　is　used　to　process　the　raw　data　so　that　the　model　focuses　on　features　with　high　impact　factors.　The　model　captures　the　features　based　on　vector　representation　and　clusters　the　features　in　multiple　directions　by　a　dual　routing　algorithm.　It　adopts　two　strategies,　namely　residual　connectivity　and　noise　capsules,　to　stabilize　the　dynamic　routing　process　to　mitigate　the　interference　of　noisy　features.　Finally,　experiments　are　conducted　on　the　NSL-KDD　dataset　and　CICIDS2017　dataset,　and　the　results　show　that　the　accuracy　is　up　to　90.31%　and　99.23%,　respectively.　©　2022,　Science　Press.　All　right　reserved.