提出了一种利用Hopfield神经网络进行交叉线识别的算法。在本算法中，一个含有交叉线的图象被映射到Hopfield神经网络之上，每一个象素与一个神经元相对应，神经元间的权重由其对应的象素间的关系（包括距离、斜率等）所决定。在Hopfield神经网络的收敛过程中，神经元的状态根据一定的规则不断地被调整直到收敛；同时随着神经元状态的确定，交叉线段也被识别出来。在实验模拟中，此算法显示了优越的性能。

An algorithm using Hopfield neural network to recognize intersection lines is presented in this paper.This algorithm utilizes a new concept which is much different from the conventional processing methods of intersection lines recognition. In this algorithm, an image containing intersection lines is mapped to a Hopfield neural networks.Every pixel in the image is mapped to a neuron, and the lines between neurons are determined by the relations of the represented pixels such as the distance, potentials difference and slope. During the Hopfield neural nets evaluation, the neurons states are modified till convergence. Along the settlement of the neurons' states, the mapped intersection lines is recognized. During experimental simulations, this algorithm really showed fine performance.