This paper mainly discusses the internal correlation between meshless discrete data and learning samples, meshless dynamic analysis recursive operation and information transmission mode in cyclic convolutional neural networks. This paper establishes a cyclic convolutional neural network based on the meshless method. This paper demonstrates an agent model of cyclic convolutional neural network based on dynamic characteristics. This method combines the advantages of the flexible configuration of meshless nodes in the discrete model. The universality and adaptability of cyclic convolutional neural networks are improved. In addition, because of the unique historical memory characteristics of the periodic module, it can analyze continuous data efficiently. The solution of dynamic analysis is accelerated without affecting the calculation accuracy. Based on a group of examples, the accuracy and effectiveness of this method are studied experimentally.