Drug-Target Interaction （DTI） prediction is a key task in drug discovery and repurposing. The challenge lies in how to integrate multi-source heterogeneous features to characterize the complex relationships between drugs and targets comprehensively. To address the shortcomings of traditional methods that rely on a single data source and model complex nonlinear relationships in a low-quality way， a DTI prediction method based on Structure-Network collaborative features and grid-attention enhanced KAN （Kolmogorov-Arnold Network） （SNKDTI） was proposed. Firstly， a feature extraction strategy based on structure and network collaboration was designed： for drug representation， molecular fingerprints were fused with graph embedding methods to quantify chemical structures； for target representation， traditional physicochemical encoding was combined with pre-trained models to extract sequence features. Meanwhile， heterogeneous networks such as drug-disease associations and protein-protein interactions were introduced， network topological features were extracted by using the Random Walk with Restart （RWR） algorithm， and the features were compressed by using a Denoising AutoEncoder （DAE）， so as to integrate structural and network information of drugs and targets. Secondly， a Heterogeneous Biological Information Network （HBIN） was constructed to carry out feature propagation by using a Graph Convolutional Network （GCN）. Additionally， a Grid-Attention enhanced KAN （GA-KAN） was proposed， which introduced multiple learnable B-spline basis function grids and attention mechanisms to achieve adaptive combinations of multiple nonlinear mapping modules， thereby enhancing the model’s expressive power and input adaptability. Finally， a Gradient Boosting Decision Tree （GBDT） was used to build an end-to-end prediction framework. Experimental results of comparing the proposed method and benchmark methods on public datasets show that SNKDTI achieves improvements of 0.81%， 1.36%， and 3.29% in Area Under the receiver operating characteristic Curve （AUC）， Area Under the Precision-Recall curve （AUPR）， and F1-score， respectively， over the best-performing benchmark methods. The above prove that SNKDTI enhances accuracy， robustness， and generalization ability significantly， providing an efficient tool for new drug target screening.