Recently, machine learning has been widely used for services classification that plays a crucial role in services discovery, selection, and composition. The current methods mostly rely on only one data modality (e.g. services description) for web services classification but fail to fully exploit other readily available data modalities (e.g. services names, and URL). In this paper, a novel MultiModal-Attention-based deep neural network (MMA-Net) is proposed to facilitate the web services classification task via effective feature learning from multiple readily available data modalities. Specifically, a new multimodal feature learning module is introduced to achieve effective message passing and information exchanging among multiple modalities. We conduct experiments on the real-world web services dataset using various evaluation metrics, and the results show that our framework achieves the state-of-the-art results.