In recent years, big data and deep learning technology have been extensively applied in the field of financial risk identification. Many scholars have used deep learning technology to provide early warning of the time series of the risk in agricultural credit guarantee. The traditional model training mainly relies on the evaluation of the financial and non-financial situation of new agricultural business entities to identify the risk in agricultural credit guarantee. Therefore, the selection of influencing factors, model construction and the optimization algorithm are usually highly subjective. And the model is built on the basis of wired network to analyze the risk data, the risk data can not be long-distance transmission. Improving these influencing factors can help to provide more scientific suggestions on the training of the model for the identification of risk in agricultural credit guarantee, and it can also significantly enhance the risk identification accuracy of the model. In this paper, under the wireless network, PSO-SVM was used to model the real data of 510 types of new agricultural operating entities in the agricultural credit guarantee system of L province from 2017 to 2019, and to identify the risks. Finally, a high precision risk identification model of agricultural credit guarantee based on PSO-SVM under wireless network was established, and its accuracy of the identification results was compared with that of the unimproved SVM model. With 14 influencing factors in the test set as input, and a test ratio of 0.3, the following conclusions were obtained: first, after improving the parameter selection and algorithm of the model for the identification of the risk in agricultural credit guarantee based on the traditional SVM, the accuracy of the improved model is higher than that of the traditional model. Second, after adding the influencing factor, namely the cognitive bias of the new agricultural business entities in agricultural product market forecasting, the accuracy of agricultural credit guarantee risk identification model based on PSO-SVM under wireless network reached 92.2%, indicating that the model is well trained and can accurately identify the risk of agricultural credit guarantee in practice.