Online service systems struggle with accurately and quickly pinpointing and resolving failures within their intricate systems, and it therefore emerges the solutions for fault localization in the code. However, the previous fault localization models suffer from low localization accuracy and poor interpretability due to the complex dependencies among fault characteristics in industrial practice. To address this issue, challenges brought by the long-distance dependencies among fault features and the unbalanced distribution of fault knowledge, and to improve the interpretability of the model, we present a fault localization model in online service systems more actionable and interpretable, named FL-AIer. Specifically, FL-AIer consists of two components: the feature encoding component and the fault localization component. The feature encoding component utilizes graph attention networks to capture the complex spatio-temporal dependencies within fault features. Then, the fault localization component adopts a three-stage approach, leveraging a multi-attention mechanism to identify and prioritize the most relevant fault features for precise localization. Additionally, the Fault Knowledge Balancing module it contains introduces a weighted Kullback-Leibler divergence loss function to ensure that the model pays adequate attention to all fault features, addressing the issue of imbalanced fault knowledge distribution and enhancing localization performance. We conducted extensive experiments on four datasets, and the results demonstrated that FL-AIer effectively addressing the challenges of fault localization in online system environments, and consistently outperforms the state-of-the-art methods across various evaluation metrics such as A@1, A@2, A@3, A@5, and MAR. For instance, FL-AIer achieves significant improvements of 5.82%, 10.77%, 4.20%, and 15.56% on the A@1 metric, respectively. These results fully demonstrate the excellent effectiveness of FL-AIer in effectively addressing the challenges of fault localization in online system environments, surpassing the performance of existing state-of-the-art methods.