Conversational Chatbot is a critical technology for managing customer service on E-commerce platforms. However, most Chatbots struggle with data scarcity and sparsity. To tackle these issues, a novel two-step (pretrain and fine-tune) click-through-rate prediction model called knowledge-enhanced deep cross network (K-DCN) is proposed. First, a billion-scale conversation knowledge graph is constructed, inspired by real-world application scenarios. Then, the conversation knowledge graph is pretrained to capture the semantic and structure information respectively. Our K-DCN leverages novel user-state and dialogue-interaction representations from the pre-trained conversation knowledge graph, integrating them with the conventional dense and sparse feature representations within the Deep Cross Network framework. This integration is designed to perform item ranking in recommendation. From the experiments, our proposed K-DCN significantly outperforms the baseline models, and the model has been deployed in real world application.