To address the challenge of balancing convergence and diversity in Multi- Objective Evolutionary Algorithm （MOEA） when solving Many-objective Optimization Problem （MaOP）， a SubRegion-based Many-Objective Evolutionary Algorithm （SR-MaOEA） was proposed. In this algorithm， distribution structure of the objective space was constructed through a subregion partitioning strategy， the subregion density was quantified using Shifted Density Estimation （SDE）， and a hierarchical subregion dominance relation ranking strategy was designed to enhance the selection pressure of individuals. Furthermore， a convergence-diversity adaptively fused weighted selection mechanism was proposed， in which the potential value of each subregion was evaluated by calculating the difference in weighted sums of adjacent generations of subregions dynamically， and then the individuals in subregions with higher potential values were retained preferentially to update the population. Experimental results on the MAF benchmark test set comparing multiple mainstream many-objective evolutionary algorithms show that on most test problems， SR-MaOEA outperforms the comparison algorithms in terms of both Inverted Generational Distance （IGD⁺） and HyperVolume （HV） metrics， thereby demonstrating the effectiveness and robustness of this algorithm in high-dimensional objective spaces.