Network　intrusion　detection　system　can　effectively　detect　network　attack　behaviour,　which　is　very　important　to　network　security.　In　this　paper,　a　multiclassification　network　intrusion　detection　model　based　on　convolutional　neural　network　is　proposed,　and　the　algorithm　is　optimized.　First,　the　data　is　preprocessed,　the　original　one-dimensional　network　intrusion　data　is　converted　into　two-dimensional　data,　and　then　the　effective　features　are　learned　using　optimized　convolutional　neural　networks,　and,　finally,　the　final　test　results　are　produced　in　conjunction　with　the　Softmax　classifier.　In　this　paper,　KDD-CUP　99　and　NSL-KDD　standard　network　intrusion　detection　dataset　were　used　to　carry　out　the　multiclassification　network　intrusion　detection　experiment;　the　experimental　results　show　that　the　multiclassification　network　intrusion　detection　model　proposed　in　this　paper　improves　the　accuracy　and　check　rate,　reduces　the　false　positive　rate,　and　also　obtains　better　test　results　for　the　detection　of　unknown　attacks.