Author name disambiguation (AND) is a central task in academic search, which has received more attention recently accompanied by the increase of authors and academic publications. To tackle the AND problem, existing studies have proposed various approaches based on different types of information, such as raw document features (e.g., co-authors, titles, and keywords), the fusion feature (e.g., a hybrid publication embedding based on multiple raw document features), the local structural information (e.g., a publication's neighborhood information on a graph), and the global structural information (e.g., interactive information between a node and others on a graph). However, there has been no work taking all the above-mentioned information into account and taking full advantage of the contributions of each raw document feature for the AND problem so far. To fill the gap, we propose a novel framework named EAND (Towards Effective Author Name Disambiguation by Hybrid Attention). Specifically, we design a novel feature extraction model, which consists of three hybrid attention mechanism layers, to extract key information from the global structural information and the local structural information that are generated from six similarity graphs constructed based on different similarity coefficients, raw document features, and the fusion feature. Each hybrid attention mechanism layer contains three key modules: a local structural perception, a global structural perception, and a feature extractor. Additionally, the mean absolute error function in the joint loss function is used to introduce the structural information loss of the vector space. Experimental results on two real-world datasets demonstrate that EAND achieves superior performance, outperforming state-of-the-art methods by at least +2.74% in terms of the micro-F1 score and +3.31% in terms of the macro-F1 score.