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Anomaly detection in images is a very important research topic in computer vision. It can be defined as a one classification problem; for the large-scale and high-dimensional characteristics of image data sets, a novel anomaly detection model is proposed CAE-OCSVM, which is a combination of deep convolutional autoencoder (CAE) and a kernel approximate one-class support vector machine (OCSVM). The deep CAE in the model is responsible for learning the essential feature representation of the image, and then uses random Fourier features to perform kernel approximation to the essential features learned by the CAE. After the kernel approximation, the linear OCSVM performs anomaly detection on the image, and the kernel approximation technology overcomes the problem of high time complexity of kernel learning technology. Meanwhile the CAE and the kernel approximation OCSVM achieve end-to-end learning through the gradient descent method. The AUC performance of the model was tested and verified on four public image benchmark data sets. At the same time, AUC performance was compared with other commonly used anomaly detection models under different anomaly rates. Experimental result confirm that the performance of the CAE-OCSVM model on the four public image data sets is better than other anomaly detection models, indicating that the CAE-OCSVM model is more suitable for large-scale high-dimensional data set anomaly detection.