A property graph is a popular graph data model that has been widely used in various graph systems. However, when coming to graph analysis workloads, graph database systems for transactional workloads encounter challenges in terms of high latency. Traditional graph analysis systems are geared towards simple graph models and have limited transactional workload support of graphs. Therefore, there is a growing demand for a graph storage system that can efficiently handle both graph analysis tasks and transactional workloads on property graphs. The emergence of persistent memory provides people with an opportunity to redesign graph storage systems to fully leverage the advantages of this device. To this end, this study proposes TAG, a persistent memory-based property graph storage system. TAG adopts a novel hybrid architecture for graph storage to fully utilize the advantages of persistent memory and main memory. Secondly, by combining topology and index into one, TAG embeds the graph topology into the system index to accelerate queries on the graph topology. Finally, by organizing the graph's property data based on labels, TAG further optimizes access to graph properties. Experimental results show that TAG is significantly better than other graph database systems and has comparable performance to graph analysis systems.