Image anomaly detection aims to identify and locate the abnormal region in an image. To address the issue on the insufficient utilization of different-level feature information in the existing methods, an image anomaly detection method based on multi-level feature fusion network was put forward. By using the pseudo anomaly data generation algorithm incorporated with the anomaly prior knowledge, the anomaly data of the training set were augmented, and then the anomaly detection task was transformed into a supervised learning task. A multi-level feature fusion network was constructed to enriches the low-level texture information and high-level semantic information of features by fusing the different levels of features in the neural network, which could make the features used for anomaly detection more discriminative. In the training phase, the score constraint loss and the consistency constraint loss were designed and combined with the feature constraint loss to train the whole network model. Experimental results on the MVTec dataset showed that the proposed model could achieve 98.7% AUROC (Area Under the Receiver Operating Characteristic) in the detection task, 97.9% AUROC in the pixel-wise localization task and 94.2% Per-Region-Overlap in the localization task, which outperformed several existing anomaly detection approaches.