With the popularization of information technology, the dramatic growth of data size, the significant rise in data dimensions, the increasing complexity of data types, and the diversity and complexity of the manifestations of anomalies all make anomaly detection more difficult. In this paper, we enhance the structure of the variational self-encoder in deep neural networks to maintain the benefits of anomaly detection technology, which relies on reconstruction error. Utilizing the high reliability of agent supervision to make up for the lack of detection performance of the deep self-encoder when the anomaly data dimension is high, a novel unsupervised anomaly detection model for high-dimensional and high anomaly rate data is proposed. Results from tests on several public datasets show that the UAD-ADC model does better than four other traditional anomaly detection methods in all of the datasets when looking at AUC values. On two datasets, Pendigits and Letter, the method improves by 0.3648 and 0.3004 over the lowest algorithm, and the experimental results confirm its effectiveness.