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In a write-intensive work environment, log-structured-merge (LSM-Tree) has gradually become the mainstream storage system, LSM-tree exists problems such as slow read operation speed, high cost of write operation, and low efficiency of range query operation, etc. In view of these problems, a study is carried out to improve the performance of LSM-tree, and a strategy to optimize the read and write performance of key-value storage system based on LSM-tree is proposed. a read and write performance optimization strategy for LSM-tree-based key-value storage system, designing the vTree structure through the key-value separation strategy, and proposing the combination of intra-tier subsumption and negative inter-tier merging, as well as the strategy of range query-optimized merging, so as to optimize the range query performance of the system, and adopting different compression structures in the LSM-tree and the vTree in order to achieve the system's read and write performance improvement; the experimental results show that the read performance is improved by 30% compared to RocksDB, and the range query performance is improved by 10% compared to RocksDB-vTree.