The invention provides an equipment evaluation and federated learning importance aggregation method based on edge intelligence, which comprises the following steps of cloud server initialization: generating an initial model by a cloud server, equipment evaluation and selection: receiving resource information of terminal equipment by an edge server, generating a resource feature vector, and inputting the resource feature vector to the evaluation model, local training: after the edge server selects the intelligent equipment, sending the transferred initial model to the intelligent equipment, andenabling the intelligent equipment to carry out local training on the initial model in federated learning to obtain a local model, local model screening: sending the local model to an edge server, and judging whether the local model is an abnormal model or not by comparing the loss values of the local model and a previous round of global model, and global aggregation: performing global aggregation by using a classical federated average algorithm. According to the method provided by the invention, on one hand, the training bottleneck problem with resource constraint equipment is solved, and onthe other hand, the model aggregation effect is improved so as to reduce redundant training and communication consumption.