In a natural dialogue, humans can handle misunderstanding, non-understanding, and sudden topic change integrally. An essential aspect of human-machine interaction is natural language understanding (NLU). This work proposes a hybrid model for NLU combining feature extraction with indicator classes (syntactic tokens and sequences) and semantic similarity for automatic labelling and a deep CNN learning model to integrally detect a sudden topic change, misunderstanding and non-understanding. The results report a significant improvement for the convolution model compared to the baseline multi-layer perceptron model for the classification task.