Aiming to address the unsatisfactory expression ability of semantics， which results in inaccurate text descriptions in video captioning， a chained semantic generation network （ChainS-Net） for video captioning is proposed. A multistage two-branch crossing chained feature extraction structure is constructed that uses global and local domain modules as basic units and captures the video semantics from global and local visual features， respectively. At each stage of the network， semantic information is transformed and parsed between the global and local domains. This method allows visual and semantic information to be cross referenced and improves the semantic expression ability. Furthermore， it allows a more effective semantic representation to be obtained through multistage iterative processing， thereby improving video captioning. Experimental results on MSR-VTT and MSVD datasets show that the proposed ChainS-Net outperforms other similar algorithms. Compared with the semantics-assisted video captioning network， SAVC， ChainS-Net shows average improvements of 2.5% in four metrics of video captioning.