Anomaly detection in multivariate time series is a challenging problem that requires models to learn information representations from complex temporal dynamics and derive a distinguishable criterion that can identify a small number of outliers from a large number of normal time points. However, in time series analysis, the complex temporal correlation and high dimensionality of multivariate time series will result in poor anomaly detection performance. To this end, this study proposes a model based on MLP (multi-layer perceptron) architecture (UMTS-Mixer). Since the linear structure of MLP is sensitive to order, it is employed to capture temporal correlation and cross-channel correlation. A large number of experiments show that UMTS-Mixer can detect time series anomalies and perform better on the four benchmark datasets. Meanwhile, the highest F1 is 91.35% and 92.93% on the MSL and PSM datasets, respectively.