With the in-depth research on many-objective optimization problems， many-objective optimization problems with irregular Pareto frontiers pose challenges to existing methods due to their complex Pareto frontiers distribution. To address the above issues， a many-objective evolutionary algorithm based on the enhanced growing neural gas is proposed. This algorithm combines the learning characteristics of growing neural networks with the optimization characteristics of binary quality indicators to enhance the convergence pressure of the population at the irregular Pareto frontier. Firstly， an enhanced growing type of neural gas network is designed， which utilizes the topological information of the Pareto optimal frontier to guide the population to converge towards the Pareto optimal frontier direction. Then， a joint metric is proposed to comprehensively evaluate the convergence of individuals in conjunction with Pareto dominance information. Finally， an adaptive reference point based environment selection is proposed to enhance the diversity of the population in high-dimensional target space. To verify the performance of the proposed algorithm， 44 irregular many-objective optimization problems in the DTLZ and WFG benchmark problem sets are compared with five advanced many-objective evolutionary algorithms. Experimental results show that the overall performance of the proposed many-objective evolutionary algorithm based on enhanced growing neural gas is superior to the comparison algorithms.