Network　intrusion　detection　plays　an　important　role　as　tools　for　managing　and　identifying　potential　threats,　which　presents　various　challenges.　Redundant　features　and　difficult　marking　in　data　cause　a　long-term　problem　in　network　traffic　detection.　In　this　paper,　we　propose　a　semi-supervised　machine　learning　framework　based　on　multi-strategy　feature　filtering,　principal　component　analysis　(PCA),　and　an　improved　Tri-Light　Gradient　Boosting　Machine　(Tri-LightGBM)　based　on　stratified　sampling.　This　multi-strategy　feature　filtering　method　employing　Fisher　score　and　Information　gain　can　select　features　that　have　good　category　discrimination　and　are　more　relevant　to　category　labels.　After　that,　we　combine　PCA　to　convert　multiple　features　into　comprehensive　features,　which　are　used　as　the　input　of　the　Tri-LightGBM　model.　Tri-LightGBM　can　exploit　unlabeled　data　cooperatively　and　maintain　a　large　disagreement　among　the　base　learners.　Moreover,　we　propose　a　stratified　sampling　based　on　labeled　categories　to　reduce　the　probability　of　being　selected　as　the　same　category　during　the　model　update　process.　Thus,　the　Tri-LightGBM　based　on　stratified　sampling　can　compensate　for　the　classification　error　rate　caused　by　the　imbalance　of　the　dataset.　The　semi-supervised　machine　learning　framework　is　evaluated　on　two　intrusion　detection　evaluation　datasets,　namely　UNSW-NB15　and　CIC-IDS-2017.　The　evaluation　results　show　that　the　multi-strategy　feature　filtering　method　can　increase　the　accuracy,　recall,　precision,　and　F-measure　by　up　to　0.5%,　and　reduce　the　false-positive　rate　by　up　to　0.5%.　Furthermore,　the　precision　rate　of　minority　categories　can　be　increased　by　about　1-2%.