With　the　increasing　diversity　of　network　attacks,　the　security　of　big　data　platforms　is　receiving　more　and　more　attention.　To　solve　the　problem　of　detecting　and　classifying　attack　events　on　unlabeled,　multi-source　heterogeneous　big　data　platform　log　data,　this　paper　proposes　a　semi-supervised　security　event　detection　and　classification　identification　model　based　on　a　time-series　detection　algorithm　and　UEBA.　First,　based　on　data　analysis　and　processing　and　security　event　knowledge　base　construction,　a　time　series　detection　algorithm　is　used　to　detect　anomalies　in　some　log　data.　Based　on　the　anomaly　identification　results,　a　fine-grained　analysis　guide　rule　encoding　module　is　conducted　to　initially　label　the　anomaly　results.　Then,　semi-supervised　learning　is　performed　on　a　small　amount　of　labeled　data　by　the　Pu　Learning　algorithm　to　train　an　optimized　detection　model　to　achieve　anomaly　identification　of　unlabeled　data.　Finally,　based　on　the　classification　results,　the　XGBoost　algorithm　is　further　used　to　train　the　recognition　results　for　multi-classification　to　enhance　the　real-time　detection　and　prediction　capability　of　subsequent　related　attacks.　The　experimental　results　show　that　the　proposed　model　can　effectively　identify　anomalous　intrusion　detection　sequences　and　obtain　better　classification　results.　©　2023　IEEE.