Localizing　the　root　cause　of　network　faults　is　crucial　to　network　operation　and　maintenance.　Operational　expenses　will　be　saved　if　the　root　cause　can　be　identified　accurately.　However,　due　to　the　complicated　wireless　environments　and　network　architectures,　accurate　root　cause　localization　of　network　falut　meets　the　difficulties　including　missing　data,　hybrid　fault　behaviors,　and　short　of　well-labeled　data.　In　this　study,　global　and　local　features　are　constructed　to　make　new　feature　representation　for　data　sample,　which　can　highlight　the　temporal　characteristics　and　contextual　information　of　the　root　cause　analysis　data.　A　hybrid　tree　model　(HTM)　ensembled　by　CatBoost,　XGBoost　and　LightGBM　is　proposed　to　interpret　the　hybrid　fault　behaviors　from　several　perspectives　and　discriminate　different　root　causes.　Based　on　the　combination　of　global　and　local　features,　a　semi-supervised　training　strategy　is　utilized　to　train　the　HTM　for　dealing　with　short　of　well-labeled　data.　The　experiments　are　conducted　on　the　real-world　dataset　from　ICASSP　2022　AIOps　Challenge,　and　the　results　show　that　the　global　and　local　feature　based　HTM　achieves　the　best　model　performance　comparing　with　other　models.　Meanwhile,　our　solution　achieves　third　place　in　the　competition　leaderboard　which　shows　the　model　effectiveness.　©　2022　-　IOS　Press.　All　rights　reserved.