Sleep staging has attracted much attention as an important method for studying sleep disorders in recent years. The majority of the current automatic sleep staging methods focus on studying time-domain information and ignore the interrelation between features, resulting in low sleep classification accuracy. To solve these problems, a multi-scale features and attention gated multi-layer perceptron mechanisms named MA-SleepNet is proposed for automatic sleep stage classification, using single-channel electroencephalogram (EEG) signals. The network consists of a multi-scale feature extraction (MFE), squeeze and excitation network (SE), and an attention gated multi-layer perceptron mechanism(aMLP). The MFE module uses convolutional kernels of different sizes to fully extract different scale features from EEG signals. The SE module further optimizes the weight of features and improves the feature expression ability of the network. The aMLP module combines multi-layer perceptron with gating mechanism, adds tiny self-attention mechanism to realize data communication between different dimensions and integrates powerful feature representation.The MA-SleepNet model is evaluated on two public datasets, Sleep-EDF-20 and Sleep-EDF-78. It achieves the accuracy of 86.1% and 83.2% on the Fpz-Cz channel, respectively. Compared with the existing sleep staging methods, our method improves the classification performance.