The research shows that the existing multi-objective evolutionary algorithms are difficult to effectively balance the convergence and diversity of the population when dealing with optimization problems with different Pareto fronts. To address the above situation, this paper proposes an adaptive reference vector and reference point based many-objective evlolutionary algorithm(ARVRPMEA), which mainly uses population sparsity to adaptively adjust reference vectors and reference points to improve population diversity. First, the ARVRPMEA generates a uniformly distributed subset of reference vectors and a subset of reference points, and uses this subset of reference vectors to decompose the population. Then, new reference vectors and reference points are generated according to the distribution of solutions in the largest subpopulation until the scale of the reference vector set and reference point set is satisfied. Finally, to further improve population convergence, the algorithm combines the metrics for environmental selection to preserve the individuals with higher convergence into the next generation of populations. The experimental results show that the ARVRPMEA has good performance in solving problems with different Pareto fronts.