Sentiment　classification　of　short　text　is　a　challenging　task　because　of　limited　contextual　information.　We　propose　a　capsule-based　hybrid　neural　network　model　which　can　obtain　the　implicit　semantic　information　effectively.　Bidirectional　gated　recurrent　unit　(BGRU)　is　applied　in　this　model　to　achieve　the　interdependent　features　with　long　distance.　Moreover,　the　capsule　network　can　extract　richer　textual　information　to　improve　expression　ability.　Compared　with　the　attention-based　model　which　combines　self-attention　mechanisms　and　convolutional　neural　networks　(CNN),　the　capsule-based　hybrid　model　has　the　advantage　of　less　training　time　and　simple　network　structure　to　achieve　better　performance.　The　performance　is　evaluated　on　two　short　text　review　datasets.　Our　capsule-based　model　outperforms　other　related　models　on　movie　review　data　and　gets　the　highest　accuracy　of　0.8255.　Meanwhile,　it　performs　better　than　most　of　the　systems　in　NLPCC2014　Task　II　and,　especially　achieves　the　best　result　on　negative　data.