本文对大脑中信息痕迹量自然衰减建立指数回归方程，实测了多次重复学习后记忆衰减曲线，并建立了相应地学习与记忆模型。结果表明：人脑对某事物的信息痕迹量是一个指数衰减函数。但每重新学习一次，大脑中的信息痕迹量就有一次阶跃。其阶跃的幅度依赖于学习系数、回忆系数与重复学习的次数ｎ。 In this paper, we establish an exponential regression equation for the natural attenuation of the amount of information traces in the brain, measure the memory decay curve after repeated learning, and establish a corresponding learning and memory model. The results show that the amount of information trace of human beings is an exponential decay function. But with each new study, there is a step in the amount of information in the brain. The magnitude of its step depends on the learning coefficient, the recall coefficient and the number of repetitive learning n.