With　the　rapid　development　of　the　Internet,　various　network　invasive　behaviors　are　increasing　rapidly.　This　seriously　threatens　the　economic　development　of　individuals,　enterprises,　and　society.　Network　intrusion　detection　is　important　in　network　security　systems,　which　can　be　regarded　as　a　classification　problem.　It　aims　to　distinguish　between　the　specific　categories　of　various　network　behaviors　and　determine　whether　the　behavior　belongs　to　network　intrusion.　However,　network　intrusions　present　a　diverse　and　fast-changing　trend,　making　categorizing　difficult.　Due　to　feature　redundancy,　uneven　distribution　of　sample　numbers,　and　inefficient　parameter　optimization,　traditional　rule-based　approaches　fail　to　achieve　satisfying　classification　accuracy.　This　work　proposes　a　multi-classification　intrusion　detection　model　based　on　Stacked　Sparse　Shrink　AutoEncoder　(SSSAE),　Genetic　Simulated　annealing-based　particle　swarm　optimization　optimized　Tabnet　classifier　(GS-Tabnet),　and　Decision　Fusion　(DF),　called　for　SGTD　short.　Among　them,　SSSAE　extracts　multiple　feature　sets　from　the　input　data.　Then　GS-Tabnet　trains　a　classifier　for　each　feature　set.　Finally,　the　decision　fusion　fuses　the　results　from　these　classifiers　to　obtain　the　final　classification　result.　SGTD　is　compared　with　eight　multi-classification　benchmark　models,　and　its　intrusion　detection　accuracy　is　superior　to　its　peers.　©　2024　IEEE.