Objective This study explores algorithm performance estimation accuracy and efficiency of different partial correlation coefficient estimation methods under high-dimensional non-sparse conditions. Methods Existing research on Pcor estimation methods primarily focuses on the existence of partial correlation relationships in high-dimensional data under sparse assumptions. However research on algorithm efficiency and estimation accuracy of Pcor estimation methods under non-sparse conditions is relatively lacking. This study first comprehensively considered partial correlation coefficient estimation methods applicable to non-sparse conditions and employed regularization methods to handle corresponding high- dimensional regression models. Further exploration was conducted to investigate the estimation methods?? performance and efficiency regarding partial correlation coefficients. To verify the estimation performance of different algorithms extensive numerical simulation experiments were conducted and real data from the stock market were analyzed. Results Under high-dimensional non-sparse conditions both unbiased adaptive LASSO and asymptotically unbiased MCP performed excellently in estimating partial correlation coefficients. Conclusion Under high-dimensional non-sparse conditions partial correlation coefficient estimation methods exhibit similar characteristics to those under high-dimensional sparse conditions accurate estimation when Pcor is negative and some bias when Pcor is positive. In terms of regularization method selection the comprehensive performance of unbiased adaptive LASSO and asymptotically unbiased MCP methods is superior to the corresponding biased LASSO methods. Specifically under small sample sizes the performance of the adaptive LASSO·RES algorithm is superior while under large sample sizes MCP·REG2 performs better with REG2 being most effective when Pcor is positive. It is worth noting that controlling variables is more challenging and impactful under non-sparse conditions while controlling variables are effectively controlled under sparse conditions. Therefore as non-sparse conditions approach sparse conditions algorithmic errors decrease and efficiency increases. Under appropriate non-sparse conditions unbiased adaptive LASSO · RES and asymptotically unbiased MCP · REG2 algorithm perform well exhibiting good robustness and stability. Under stronger non-sparse conditions the adaptive LASSO·RF algorithm performs the best.