The social network is complicated with different evolution mechanisms.It is of great significance to reasonably analyze social network evolutions and effectively detect social events.The event detection methods based on link prediction make most of the limited network topological information, discover the network evolution fluctuation, and detect events.However, most of existing methods are limited by the assessment measures of link prediction, and neglect the otherness of micro node evolution mechanisms.They use the same similarity index to describe evolution fluctuations of different nodes, which is adverse to the performance of event detection.To improve the accuracy and sensitivity of event detection, this paper proposes an event detection method based on node evolution fluctuation for social networks (NodeED).The method consists of a node similarity index judgement algorithm (SimJudge) and a micro evolution fluctuation detection algorithm (MicroFluc).The main work is as follow: (1) Based on the particle swarm algorithm, SimJudge is proposed to compare the description performances of different similarity indexes for a node evolution fluctuation.Different nodes can find their optimal similarity indexes at different periods by SimJudge; (2) To quantify the effect of events, MicroFluc is proposed to consider the diversity of node evolution fluctuations and evaluate the entire network evolution fluctuation; (3) In real social networks VAST and ENRON, NodeED results in the event detection sensibility increase by 100% in VAST and 50% in ENRON, which shows NodeED has more advantages to detect events in social networks than other methods.