Abstract: Fritillaria is a widely used traditional Chinese medicine, with a complex source and a wide variety of medicinal materials. Different varieties have similar external characteristics, making it difficult to distinguish using traditional methods. To achieve rapid and objective identification of Fritillaria species, this study proposed a method for rapid identification based on electronic tongue and electronic eye combined with a deep learning model. Electronic tongue and electronic eye were utilized to collect gustatory fingerprint and visual image information from different categories of Fritillaria, respectively. An enhanced Transformer encoder based on causal attention mechanism was employed to extract time-series features from the ET signals and augment the ability to extract local features. Meanwhile, an improved ShuffleNetV2 network based on coordinate attention mechanism was used to extract morphological features of EE image and suppress background noise. Subsequently, a feature weighted fusion module was presented to adaptively integrate the feature information extracted from both the electronic tongue and electronic eye, and achieve classification and recognition of the fused features. The experimental results indicated that the proposed information fusion method had better classification performance compared to separate usage of electronic tongue and electronic eye, with a testing accuracy of 98.4%. This study provides a novel approach for rapidly identifying Fritillaria varieties, which offers research insights into the classification and traceability analysis of other Chinese medicinal materials.