To achieve the construction of a Chinese cyber threat intelligence knowledge graph, a multi-classification approach was explored that combined an enhanced focal loss with a multi-scale convolutional neural network. This approach was used to extract entity relationships from the text. To address the long-tail problem within Chinese cyber threat intelligence relation extraction, the Focal loss function was improved to enhance the learning of challenging samples. To tackle the problem of significant variance in keyword lengths caused by the mixture of Chinese and English vocabulary and the abundance of specialized terms in intelligence, a multi-granularity convolutional neural network was employed to capture features of statements at different scales. This approach enhanced the model's performance in the task. The comparative experiments demonstrate that the Weighted-F1 score and accuracy of the proposed MCNNFL model are significantly higher compared to the methods commonly used in other fields. Ablation experiments indicate that both proposed methods for the problems mentioned above can improve model performance and boost performance simultaneously.