In database management systems, concurrency control manages the interleaved execution of multiple transactions, ensuring data integrity and consistency. However, in high-conflict scenarios, current strategies often lead to frequent transaction aborts, resulting in a significant waste of time on ineffective operations. To effectively address this challenge, we introduce an innovative Prior Cascading Concurrency Control (PC3) mechanism. This mechanism aims to proactively predict conflicts and minimize the performance penalty caused by these conflicts through a series of precise decisions. Specifically, PC3employs various prediction models to forecast transaction working sets, providing accurate transaction information for conflict detection. On this basis, we implemented a hash-based conflict detection method and established a cascading decision algorithm to minimize transaction abort frequency. Experimental results on the TPC-C workload show that in high-conflict scenarios with a Zipfian skew and thread counts between 5 and 40, PC3reduces the number of erroneous transactions by 18 times, and increases throughput by approximately 30.7%. compared to the best-performing optimistic methods.