For diagnosing mental health conditions and assessing sleep quality, the classification of sleep stages is essential. Although deep learning-based methods are effective in this field, they often fail to capture sufficient features or adequately synthesize information from various sources. For the purpose of improving the accuracy of sleep stage classification, our methodology includes extracting a diverse array of features from polysomnography signals, along with their transformed graph and time-frequency representations. We have developed specific feature extraction modules tailored for each distinct view. To efficiently integrate and categorize the features derived from these different perspectives, we propose a cross-attention fusion mechanism. This mechanism is designed to adaptively merge complex sleep features, facilitating a more robust classification process. More specifically, our strategy includes the development of an efficient fusion network with multi-view features for classifying sleep stages that incorporates brain connectivity and combines both temporal and spectral elements for sleep stage analysis. This network employs a systematic approach to extract spatio-temporal-frequency features and uses cross-attention to merge features from different views effectively. In the experiments we conducted on the ISRUC public datasets, we found that our approach outperformed other proposed methods. In the ablation experiments, there was also a 2% improvement over the baseline model. Our research indicates that multi-view feature fusion methods with a cross-attention mechanism have strong potential in sleep stage classification.