Most of the Multi-objective Optimization Problems （MOP） in real life are Dynamic Multi-objective Optimization Problems （DMOP）， and the objective function， constraint conditions and decision variables of such problems may change with time， which requires the algorithm to quickly adapt to the new environment after the environment changes， and guarantee the diversity of Pareto solution sets while converging to the new Pareto frontier quickly. To solve the problem， an Adaptive Prediction Dynamic Multi-objective Optimization Algorithm based on New Evaluation Index （NEI-APDMOA） was proposed. Firstly， a new evaluation index better than crowding was proposed in the process of population non-dominated sorting， and the convergence speed and population diversity were balanced in different stages， so as to make the convergence process of population more reasonable. Secondly， a factor that can judge the strength of environmental changes was proposed， thereby providing valuable information for the prediction stage and guiding the population to better adapt to environmental changes. Finally， three more reasonable prediction strategies were matched according to environmental change factor， so that the population was able to respond to environmental changes quickly. NEI-APDMOA， DNSGA-Ⅱ-A （Dynamic Non-dominated Sorting Genetic Algorithm-Ⅱ-A）， DNSGA-Ⅱ-B （Dynamic Non-dominated Sorting Genetic Algorithm-Ⅱ-B） and PPS （Population Prediction Strategy） algorithms were compared on nine standard dynamic test functions. Experimental results show that NEI-APDMOA achieves the best average Inverted Generational Distance （IGD） value， average SPacing （SP） value and average Generational Distance （GD） value on nine， four and eight test functions respectively， and can respond to environmental changes faster.