蒙特•卡罗方法是一种以概率统计理论为指导的一类非常重要的数值计算法，是一种使用随机数（或者伪随机数）来解决计算问题的方法。依托于二十世纪四十年代中期科学技术的发展和电子计算机的发明，也称之为统计模拟方法。在计算物理学（如粒子输运计算）等领域应用广泛；任何（宏观）物理系统的温度都是组成该系统的分子和原子运动的结果，这些粒子有一个不同的速度范围，而任何单个粒子的速度都因与其他粒子的碰撞而不断地变化。可是对于粒子数目巨大的情况，如果粒子处于平衡或者接近于平衡状态，这些粒子的分布却遵循着统计学规律，处于一个特定速度范围的粒子所占的比例却几乎不变。那么，我们将大量的气体分子整体在一定条件下速度分布遵循的统计规律叫做麦克斯韦速率分布律；用麦克斯韦速率分布律演示仪可以非常形象直观的演示出麦克斯韦速率分布律的曲线和物理图像。本文将采用蒙特•卡罗方法对用麦克斯韦速率分布律演示仪演示麦克斯韦速率分布的过程进行计算机模拟，并提出一些建议。将借助MATLAB R2013b作为工具。

The Monte-Carlo method is a kind of calculation method based on probability statistics theory, to use a random number (or pseudo random number) to solve the problem of the calculation. Relying on the development of science and technology and the invention of electronic computers in the forty's in twentieth Century, the statistical simulation method is also called. In Computational Physics (such as particle transport calculations), it is used widely; any (macro) physical system's temperature is the result of molecular and atomic motion, and these particles have a different speed ranges, and any single particle velocity are due to collisions with other particles and constantly changes. But for a huge number of particles, if the particle is in equilibrium or near equilibrium state, the distribution of these particles follow statistical rules, and proportion of particles in a specific speed range was almost unchanged. Then, we named the statistical rules that velocity distributions of large amounts of gas molecules in certain conditions follow Maxwell velocity distribution law; Maxwell velocity distribution law demonstration instrument can demonstrate the Maxwell velocity distribution law's curve and physical figure. This article will use the Monte-Carlo method to simulate this process with computer, and put forward some suggestions. We will use MATLAB R2013b as a tool.