With　the　rapid　development　of　network　technology,　the　Internet　has　brought　significant　convenience　to　various　sectors　of　society,　holding　a　prominent　position.　Due　to　the　unpredictable　and　severe　consequences　resulting　from　malicious　attacks,　the　detection　of　anomalous　network　traffic　has　garnered　considerable　attention　from　researchers　over　the　past　few　decades.　Accurately　labeling　a　sufficient　amount　of　network　traffic　data　as　a　training　dataset　within　a　short　period　of　time　is　a　challenging　task,　given　the　rapid　and　massive　generation　of　network　traffic　data.　Furthermore,　the　proportion　of　malicious　attack　traffic　is　relatively　small　compared　to　the　overall　traffic　data,　and　the　distribution　of　traffic　data　across　different　types　of　malicious　attacks　also　varies　significantly.　To　address　the　aforementioned　challenges,　this　paper　presents　a　novel　network　anomaly　detection　algorithm　based　on　semi-supervised　learning　and　adaptive　multiclass　balancing.　Building　upon　the　assumption　of　consistent　distribution　between　labeled　and　unlabeled　data,　this　paper　introduces　the　multiclass　split　balancing　strategy　and　the　adaptive　confidence　threshold　function.　These　innovative　approaches　aim　to　tackle　the　issue　of　the　multiclass　imbalanced　in　traffic　data.　By　leveraging　the　mutually　beneficial　relationship　between　semi-supervised　learning　and　ensemble　learning,　this　paper　presents　the　collaborative　rotation　forest　algorithm.　This　algorithm　is　specifically　designed　to　enhance　performance　of　anomaly　detection　in　an　environment　with　label　inadequacy.　Several　comparative　experiments　conducted　on　the　NSL-KDD,　UNSW-NB15,　and　ToN-IoT　demonstrate　that　the　proposed　algorithm　achieves　significant　improvements　in　performance.　Specifically,　it　enhances　precision　by　1.5-5.7%,　recall　by　1.5-5.7%,　and　F-Measure　by　1.4-4.3%　compared　to　the　state-of-the-art　algorithms.