Due to the high dimensionality and the complex variable distribution of Multivariate Time Series （MTS） data， the existing anomaly detection models generally suffer from high error rates and training difficulties when dealing with MTS datasets. Moreover， most models only consider the spatial-temporal features of time series samples， which are not sufficient to learn the features of time series. To solve the above problems， a multivariate Time Series anomaly detection model based on Multi-domain Feature Extraction （MFE-TS） was proposed. Firstly， starting from the original data domain， the Long Short-Term Memory （LSTM） network and the Convolutional Neural Network （CNN） were used to extract the temporal correlation and spatial correlation features of the MTS respectively. Secondly， Fourier transform was used to convert the original time series into frequency domain space， and Transformer was used to learn the amplitude and phase features of the data in frequency domain space. Multi-domain feature learning was able to model time series features more comprehensively， thereby improving anomaly detection performance of the model to MTS. In addition， the masking strategy was introduced to further enhance the feature learning ability of the model and make the model have a certain degree of noise resistance. Experimental results show that MFE-TS has superior performance on multiple real MTS datasets， while it still maintain good detection accuracy on datasets with noise.