Persistent memory (PM) has the characteristics of non-volatility, byte addressable, low latency, and large capacity, which breaks the boundary between traditional internal and external memory and has a has a disruptive impact on the existing software architecture. However, the current PM hardware still has problems such as uneven wear and asymmetric read and write. Especially serious I/O performance degradation problem will occur when the CPU accesses the PM across NUMA (non uniform memory access) nodes. An NUMA-aware PM storage engine optimization design is proposed and applied to Zhongxing’s new generation database system GoldenX, which significantly reduces the overhead of database system accessing persistent memory across NUMA nodes. The main innovations include: a data space distribution strategy and distributed access model across NUMA nodes are proposed under a DRAM+PM hybrid memory architecture, which realizes the efficient use of PM data space; aiming at the high latency problem of accessing PM across NUMA nodes, an I/O proxy routines access method is proposed, which converts the overhead of accessing PM across NUMA into the overhead of a remote DRAM memory copy and local access to PM. The Cache Line Area cache page mechanism is designed to alleviate the problem of I/O write amplification and improve the efficiency of local access to PM. The concept of traditional table space is extended, so that each table space has both independent table data storage and dedicated WAL (write-ahead logging) storage. For the distributed WAL storage architecture, a transaction processing mechanism based on global sequence numbers is proposed, which addresses the problem of single-point the WAL performance bottleneck, and implement NUMA-aware transaction processing, checkpoint and disaster recovery optimization mechanisms and algorithms. Experimental results show that the method proposed in this study can effectively improve the performance of the PM storage engine under the NUMA architecture, by 105%-317% in various test scenarios of YCSB and 90%-134% in TPC-C.