Selection is the main driving force of evolution and is a key feature of multi-objective evolutionary algorithms. However, when dealing with many-objective optimization problems, with the incease of objective dimensions, the conflicts between convergence and distribution will intensify, and the selection operators in the traditional multi-objective evolutionary algorithms is difficult to effectively maintain the balance between the convergence and distribution of population. To solve this problem, this paper proposes a many-objective evolution algorithm based on vector angle decomposition (VAD). Firstly, the individuals themself are used as a reference vector, and the angles between the target vectors are used as the similarity measure to estimate the population distribution, which reduces the burden of the algorithm pre-specifying the weight vectors. Then, the achievement scalarizing function is used as the convergence measure of the individual, which plays an important role in guiding the population towards the Pareto optimal front. Finally, an elite selection strategy based on vector angle decomposition is proposed, which uses vector angle information to dynamically decompose the objective space in the process of environment selection, and then uses the achievement scalarizing function to select better individuals from better distributed regions into the next generation, so as to dynamically balance the convergence and distribution of population. Comparative experiments results indicate a highly competitive performance of the proposed approach. And it has sufficient selection pressure while maintaining the diversity of the population, which can effectively guide the search of high-dimensional objective space.