Knowledge graph completion (KGC) aims to fill in missing parts of knowledge graphs (KGs) by predicting missing entities in triples. KGs embody rich and diverse real-world type information, which largely influences the representation patterns and semantic associations of entities and relations. Existing studies typically rely on dataset-specific external type information or rigid type constraints, which not only hinder their ability to adaptively track diverse type information, but also lack a mechanism to dynamically integrate type information into the embedding learning process. To address this issue, we propose a method called CTGAT. Specifically, we firstly extract entity features for clustering using a type feature extraction module. Next, we perform adaptive clustering based on the entity features of the KG and obtain cluster centroids as type features. After that, we dynamically aggregate cluster centroids with relation embeddings to capture type tendencies of entities connected with relations. Concurrently, we dynamically adjust type weights and triple weights to form a dual-attention mechanism, which distinguishes the impact of different type information on entity representations. Extensive experiments show that CTGAT achieves state-of-the-art performance on three public datasets. Particularly, Hit@1 improves by 9.0% on FB15k-237, and by 1.2% on Kinship.