With the advancement of technology, the eye tracker has developed rapidly. The eye tracker plays an important role in the interaction of the human machine interface. That is, the sampling rate of the eye tracker has been required to be highly accurate, correspondingly, how to analyze the collected gaze data has prompted worldwide concern. This paper proposes a set of tools for automatic analysis of gaze data. With the feature extraction of different conditions, the Support Vector Machine (SVM) algorithm is used to analyze the data and the evaluation has proved the feasibility of this tool. In addition, this paper also analyzes the degree of focus between the gaze data and the dynamic object. The gaze data is displayed with a dynamic and static video, and the analysis result is presented in a visual figure, which makes it easier to observe the distribution of eye movement data.