<p>Multivariate Time Series Forecasting (MTSF) is challenging due to the difficulty of extracting complex periodic patterns from temporal data. Currently, many Transformer-based models and MLP-based models achieve success by using Fourier transform (FT) to extract periodic information from time series data. However, Transformer models perform well but use many resources. MLP models are lightweight but too simple to capture complex features. Recent studies show that CNN-based models offer a better balance between efficiency and performance, but they struggle to capture both coarse and fine-grained temporal features. In addition, the single perspective of FT limits its ability to represent complex periodic features. To address these issues, we propose the Multi-Scale Fractional Fourier Transform Convolutional Neural Network (MFFCNN) for MTSF. The fractional Fourier transform (FrFT) is an extension of the FT. It extracts periodic features from different angles by adjusting the rotation. MFFCNN uses these multi-scale features to generate adaptive patches for different datasets. Then, 2D convolution is applied to model relationships within and between patches, capturing both inter- and intra-series features from a multi-scale perspective. Results on ten benchmark datasets show that MFFCNN outperforms seven state-of-the-art models. Our code is stored in https://github.com/immortalityC?tab=repositories.</p>