Nowadays， deep learning models have been widely applied in dialogue act recognition， which can improve classification performance by mining various features of dialogue acts. However， the existing methods neglect the latent association and interaction between different features of dialogue acts and also seldom consider the semantic relevance between labels of dialogue act in the classification process， which hinders from improving the performance of dialogue act recognition. To solve these problems， an MFA-HC （Multi-feature Fusion Attention-based Hierarchical Classification） method for recognizing dialogue act was proposed. Firstly， a hierarchical dialogue act classification framework based on learning without forgetting was proposed， which combined various fine-grained features such as words， parts of speech and relevant linguistic statistics to learn and train the dialogue act classification model. Secondly， a universality-individuality model based on attention mechanism was proposed to capture the universality and individuality features among different features. Experimental results on two benchmark datasets SwDA （Switchboard Dialogue Act corpus） and MRDA （ICSI Meeting Recorder Dialogue Act corpus） show that， compared with DARER （Dual-tAsk temporal Relational rEcurrent Reasoning network）， which has the current overall superior performance in existing methods， MFA-HC method improves the classification accuracy by 0.6% and 0.1% by capturing the universality and individuality features hidden in the utterance.