The main goal of the multi-objective optimization algorithm is to achieve good diversity and convergence. In traditional many-objective optimization algorithms, the selection operator is difficult to balance the convergence and diversity of the population, when the dimensionality of the objective increases. To solve this problem, this paper proposes a many-objective algorithm named an indicator-based many-objective evolutionary algorithm with adaptive boundary selection. In environmental selection, it first calculates the index $I_\varepsilon (x,y)$ of the two bodies in the population as the first selection criterion, and then proposes an adaptive boundary selection strategy, which uses population evolution information to make fuzzy predictions of hyperplane coefficients, and then approximately calculate the paradigm distance from the candidate individual to the hyperplane as the second selection criterion. Finally, the proposed algorithm is compared with five representative many-objective optimization algorithms. The experimental results show that when the algorithm handles many-objective optimization problem of the complex Pareto frontier, it can balance convergence and diversity while better maintaining diversity.