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Many keywords in a sentence that represents the semantic propensity of the sentence. These words can exist anywhere in the sentence, which poses a great challenge to sentence semantic classification. The current sentence semantic classification methods usually tackle this problem by the use of attention mechanism, and most of them utilize softmax function to calculate each word’s weight. According to the observation that a word with higher score carries more valuable information in sentence modeling, this paper presents a novel low-complexity model termed as CNN-BiGRU by integrating both convolution neural network (CNN) and bidirectional gated recurrent unit (BiGRU). Both the contextual representations and the semantic distribution are obtained through BiGRU, and the latter is constrained to a Gaussian distribution. In addition, the proposed model utilizes a shallow word-level CNN to obtain …