Online social media platforms have emerged as primary engagement channels for internet users, leading to increased dependency on social network information. This growing reliance has attracted cybercriminals, resulting in a surge of malicious activities such as spam. Consequently, there is a pressing need for efficient spam detection mechanisms. Although several techniques have been proposed for social network spam detection, spammers continually evolve their strategies to bypass these systems. In response, researchers have focused on extracting additional features to better identify spammer patterns. However, this often introduces feature redundancy and complexity, which traditional machine learning-based feature selection methods struggle to manage in highly complex datasets. To address this, we propose a novel attention network-based feature selection method that assigns weights to features based on their importance, reducing redundancy while retaining relevant information. Additionally, an adaptive Radial Basis Function Neural Network (RBFN) is employed for spam classification, enabling dynamic weight updates to reflect evolving spam behaviors. The proposed method is evaluated against state-of-the-art feature selection, deep learning models, and existing spam detection techniques using accuracy, F-measure, and false-positive rate. Experimental results demonstrate that our approach outperforms existing methods, offering superior performance in detecting spam on social networks.