For monitoring the mechanical equipment based on acoustic signal, the sound emitted during machine operation may be different due to the changes of machine operation status or malfunctions, and other factors, such as environmental noise, may also change the acoustic characteristics captured in the scene. Traditional machine anomalous sound detection systems may incorrectly label normal sounds as abnormal due to the presence of changes in acoustic features when classifying machine conditions. We propose an unsupervised machine anomalous sound detection system based on domain generalization techniques, which uses source domain data to learn common features across domains to provide generalization capability for model domain transfer conditions, multiple feature representations and specially designed subsystem architectures are used in a single neural network, combined with a domain blending method and a coordinate attention mechanism module to further improve domain generalization capability and anomaly recognition performance. Experiments are conducted on open source datasets and analyzed the comparing AUC and pAUC scores with two baseline evaluation systems, and the experimental results show that the anomaly detection performance of the proposed system in this paper is significantly improved under the domain generalization condition.