RocksDB key-value storage system based on Log-Structured Merge （LSM） tree has the problem of low read performance caused by the constraints of its hierarchical structure. One effective solution is to cache hot spot data proactively， but it faces two challenges. One is how to predict the hot spot data when the data distribution keeps on changing constantly， the other is how to integrate the proactive caching mechanism with the RocksDB storage structure. To tackle these challenges， a proactive caching framework for RocksDB key-value system with multiple components including data collection， system interaction and system evaluation was built， which can cache the hot spot data at the low levels of the LSM tree. And with the modeling of data access patterns， an incremental learning based prediction analysis method for hot spot data was designed and implemented， which can reduce the number of I/O operations of storage medium. Experimental results show that the proposed mechanism can effectively improve the read performance of RocksDB under different dynamic workloads.