To address the issues of the Differential Evolution (DE) algorithm, such as high parameter sensitivity, insufficient global exploration capability, and imbalance between exploration and exploitation processes in high-dimensional complex function optimization, an improved algorithm named Clustering Linear Combination and Optimization State Adaptive Differential Evolution (CLOSADE) , which integrates a clustering linear combination approach with an optimization state adaptive mechanism, is proposed. The research aims to enhance the algorithm's robustness and convergence performance when handling complex optimization problems. This method first designs a clustering strategy based on dual factors of fitness and distance to generate multiple clusters of linear combination vectors and introduces a dynamic distance threshold to enhance population diversity. Secondly, it constructs an Indicator of Optimization State (IOS) to quantify population distribution characteristics, driving the adaptive adjustment of mutation strategies and control parameters. Experimental results demonstrate that, on the CEC2017 and CEC2022 benchmark test functions, CLOSADE significantly outperforms advanced algorithms such as JSO, NL-SHADE-DP, and S-SAHDE-DP in terms of both convergence accuracy and speed. Particularly on high-dimensional hybrid and composite functions, CLOSADE exhibits remarkable advantages, with an average improvement of 22% in convergence accuracy and approximately 40% in convergence speed. Further population diversity analysis reveals that the multi-subgroup structure formed through clustering effectively maintains parallel search capabilities in the solution space, while the optimization state indicator ensures a dynamic balance between exploration and exploitation behaviors at different evolutionary stages of the algorithm.