Log-Structured Merge tree-based key-value (KV) stores, like LevelDB and RocksDB, are extensively applied in large-scale data storage systems. This design excels in write-intensive environments by converting random writes into sequential append operations. Despite its advantages, KV stores struggle with real-world workloads where most updates in KV pairs are infrequent. The compaction process and hierarchical data organization result in high write and read amplification. To mitigate these issues, we propose HAKV – a hotness-aware zone management approach to optimizing performance of KV stores. HAKV first separates hot KV pairs from cold KV pairs, storing hot KV pairs in dedicated zones within persistent memory (PM), enabling centralized and lightweight compaction. Second, we propose a storage zone structure in PM to achieve space optimization for cold KV pairs. Third, to bolster cache hit ratio in PM, we provide a hierarchical data framework for hot KV pairs – and a recycling strategy for invalid hot KV pairs in a zone to enhance the space utilization of PM for hot KV pairs. Finally, we design a dynamic window-based adaptive adjustment mechanism for zone pool in PM to optimize the space utilization. Thus, HAKV significantly reduces write amplification while boosting overall read and write performance. The experimental results demonstrate that HAKV achieves write amplification reduction by up to 92.3%, 79.2%, 90.2%, 41.1%, 80.6%, and 62.4% compared with LevelDB, RocksDB, NoveLSM, LightKV, Wisckey, and UniKV, respectively, with average reduction rates of 89.6%, 74.4%, 84.9% 32.3%, 63.7%, and 42.5%. Furthermore, HAKV boosts random write performance by up to 54.2×, 51.5×, 44.2×, 4.3×, 3.1×, and 4.3×, respectively—and the average improvement reaches 25.8×, 20.9×, 23.9×, 2.7×, 2.5×, and 3.4×.