Multivariate time series data often exhibit multi-scale characteristics and complex interdependencies， which makes challenges for anomaly detection. To address this issue， a multi-scale based multivariate time series anomaly detection model was developed， namely M3AD （Multi-scale Mamba Multi-layer perceptron Anomaly Detection）. Firstly， a multi-scale feature extraction method was employed， which means segmenting time series across different temporal scales to capture short- and long-term patterns. Secondly， a Multi-Layer Perceptron （MLP） and convolutional layers were utilized for feature learning for extracting local and high-level feature representations. Thirdly， a selective State Space Model （SSM） Mamba was introduced to capture key information in long sequences through its efficient processing capability. Finally， sensitive anomaly detection was achieved through a loss function based on KL （Kullback-Leibler） divergence and anomaly score calculation. To validate the model’s effectiveness， M3AD was compared with seven models， such as Anomaly Transformer and MEMTO， on four public datasets. Experimental results show that M3AD has significant advantages and leading performance compared to the other methods in key metrics such as precision， recall， and F1 score， verifying the effectiveness and superiority of M3AD in multivariate time series anomaly detection tasks.