Some scholars in the academic social network may form anomaly citation groups， and excessively cite each other’s papers for profit. Most of the existing anomaly group detection algorithms separate community detection from node representation learning， which leads to the limited performance of anomaly group detection. To deal with the issue， a Group Anomaly Detection based on Local extended community detection （GADL） algorithm was proposed. The author anomaly citation features were extracted by using semantic information such as research field and title content of the paper. An extension metric function based on node transition similarity， node community membership， citation anomaly and BFS （Breath First Search） depth was defined. The optimal anomaly detection performance could be obtained by combining anomaly community detection and anomaly node detection， and jointly optimizing them in a unified framework. Compared with ALP algorithm， the proposed algorithm improved the Area Under Curve （AUC） by 6.07%， 5.35% and 3.38% respectively on the ACM， DBLP1， and DBLP2 datasets.Experimental results on real datasets show that GADL can effectively detect academic anomaly citations.