Event detection (ED) aims to detect event triggers in unstructured text and classify them into pre-defined event types, which can be applied to knowledge graph construction, public opinion monitoring, and so on. However, the data sparsity and imbalance severely impair the system’s performance and usability. Most existing methods cannot well address these issues. This is due to that during detection, they regard events of different types as independent and identify or classify them through classifiers or space-distance similarity. Some work considers the correlation between event elements under a broader category and employs multi-task learning for mutual enhancement; they overlook the shared properties of triggers with different event types. Research related to modeling event connections requires designing lots of rules and data annotation, which leads to limited applicability and weak generalizability. Therefore, this study proposes an event-detection method based on meta-attributes. It aims to learn the shared intrinsic information contained in samples across different event types, including (1) extracting type-agnostic semantics of triggers through semantic mapping from the representations of special symbols; (2) concatenating the semantic representations of triggers and samples in each event type as well as the label embedding, inputting them into a trainable similarity measurement layer, thereby modeling a public similarity metric related to triggers and event categories. By combining these representations into a measuring layer, the proposed method mitigates the effects of data sparsity and imbalance. Additionally, the full fusion model is constructed by integrating the type-agnostic semantic into the classification method. Experiments on ACE2005 and MAVEN datasets under various degrees of sparsity and imbalance, verify the effectiveness of the proposed method and build the connection between conventional and few-shot settings.