对随机经典动量算法（CM）的收敛速度问题进行深入研究，通过对传统带动量随机梯度下降算法的迭代公式进行改造，在非强凸和光滑的条件下得到了算法的收敛阶。当动量系数p_t取常数的时候，收敛阶为O，当动量系数p_t取变系数的时候，通过设置不同的学习率，分别得到收敛速率。最后通过数值实验说明其合理性。

In this paper, we have conducted in-depth research on the convergence rate of the Stochastic Classical Momentum Algorithm （CM）. By modifying the iterative formula of traditional stochastic gradient descent algorithm with momentum, we obtain the convergence rate of algorithm with non-strongly convex and non-smooth. When the momentum coefficient p_t  is a constant, the algorithm achieves a convergence rate of O.When the momentum coefficient p_t is not a constant, we obtain the convergence rate by setting different learning rates. Finally, the rationality is demonstrated through numerical experiments.