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Knowledge-grounded dialogue generation is proposed to solve the problem of general or meaningless responses in traditional end-to-end dialogue generation methods. It generally includes two sub-modules: knowledge selection and knowledge-aware generation. Most studies consider the topic information for knowledge-aware generation, while ignoring it in knowledge selection. It may cause the topic mismatch between the overall dialogue and the selected knowledge, leading to the inconsistency of the generated response and the context. Therefore, in this study, we propose a Topic-driven Knowledge Selection method (TopicKS) to exploit topic information both in knowledge selection and knowledge-aware generation. Specifically, under the guidance of topic information, TopicKS selects more accurate candidate knowledge for the current turn of dialogue based on context information and historical knowledge information. Then the decoder uses the context information and selected knowledge to generate a higher-quality response under the guidance of topic information. Experiments on the notable benchmark corpus Wizard of Wikipedia (WoW) show that our proposed method not only achieves a significant improvement in terms of selection accuracy rate on knowledge selection, but also outperforms the baseline model in terms of the quality of the generated responses.