The detection of fake news is not confined to a single domain, and due to domain transfer, the reliance on detection methods specific to a single domain becomes inadequate when dealing with multi-domain issues. When a multi-domain detection occurs, labels from one domain focus exclusively on the news characteristics of that particular domain, ignoring the possibility that part of the news may include functions from more than one domain. To address these challenges, we propose a multi-domain fake news detection model based on fuzzy rules, incorporating a fuzzy mechanism and an optimization module for fuzzy association rules. In this model, we first introduce a fuzzy mechanism that applies fuzzy processing to domains through neural networks, constructing fuzzy domain labels for each news domain. Subsequently, we incorporate fuzzy association rules to extract a broader range of sample features, thereby enhancing detection effectiveness. Experiments conducted on a fake news dataset demonstrate that our model achieves commendable detection performance with a relatively low training cost, reaching an \({F_1}\) score of over 97.13% for sample detection. This model successfully overcomes the limitations of single-domain detection in fake news identification and improves the precision of feature extraction through fuzzy rules, showing superior performance in handling multi-domain fake news detection.