As the basics of Visual Simultaneous Localization And Mapping (VSLAM), keyframes play an essential role. In previous works, keyframes are selected according to a series of view change-based strategies for short-term data association (STDA). However, the texture enrichment of frames is always ignored, resulting in the failure of long-term data association (LTDA). In this paper, we propose an information enrichment selection strategy with an information occupancy grid model and a deep descriptor. Frame is expressed by a deep global descriptor for a statistical explainable abstraction, in which the texture enrichment is indicated. Based on the abstraction, an information occupancy grid model is established to measure the information enrichment and the potential LTDA ability. Evaluations on variant datasets are conducted, showing the advantage of our proposed method in terms of keyframe selection and tracking precision. Also, the statistical explainability of the deep descriptor is provided. The proposed keyframe selection strategy can improve LTDA and tracking precision, especially in situations with repeated observations and loop-closures.