The chemical process fault detection and diagnosis technology represented by deep learning has become one of the main ideas to solve the problem in the industry. However, the existing deep learning diagnosis methods only focus on the non-linear high-order interactive features when constructing training models and ignore the complementary of linear features and low-order interactive features to global modeling. In addition, the high-order features extracted by the existing deep models involve only implicit interactive features, whose feature forms are unknown and uncontrollable in order. Based on these problems, this work proposes a extreme deep factorization machine-based fault diagnosis method for chemical processes, which achieves automatic extraction and efficient integration of high-order, low-order and linear features by parallel fusion of three different types of network models (factorization machine, deep neural networks and compressed interaction network). First, the selected data are sequentially subjected to preprocessing operations such as Z-score normalization, label annotation, and format conversion to convert the input data into the format data required by the model. Then, the format data are simultaneously input to the three neural network models to help train the proposed diagnostic model in parallel. Finally, the fault diagnosis results are output based on the optimal diagnosis model. From the perspective of single-fault diagnosis and multi-fault hybrid diagnosis, extensive comparison experiments are conducted on the Tennessee-Eastman process (TE) simulation dataset, and the results show that the proposed method has significant advantages over previous fault diagnosis methods in terms of metrics such as precision and recall rate.