Adam is one of the most commonly used optimization algorithms at present, but it is faced with the problem that the model does not converge due to the vibration of learning rate, and its improved algorithm AMSGrad also has the problem of invalid second-order momentum caused by gradient decline. To solve the above problems, Adams optimization algorithms based on adaptive momentum update strategy was proposed. Firstly, an adaptive momentum update strategy was constructed by introducing adaptive update parameters for the first order momentum and the second order momentum, and a smaller first order momentum update parameters was used during the final parameter update phase. Secondly, based on this update strategy, a fast convergence algorithm named Adams was proposed. Finally, the convergence of Adams algorithm was analyzed theoretically. The comparative experiments based on text classification and image classification show that Adams has faster convergence speed, better training results, and more excellent generalization ability than Adam and AMSGrad. The ablation experiment proves the effectiveness of Adams adaptive momentum updating strategy.