Collaborative target tracking is an essential task in positioning systems, particularly in environments characterized by high dynamics, multisource heterogeneous data, and interactive multiagent scenarios. The challenge in such networks lies in the direct utilization of multisource heterogeneous data as feature input for models. Additionally, the presence of high-dynamic time-series data complicates the extraction of dependencies by the models. To address these issues, we introduce a novel approach that integrates a factor graph-based data fusion method with a graph neural network. This combination is designed to uncover potential dependencies between time-series data and positional information within dynamic networks. Furthermore, we employ a self-attention mechanism, enabling distance-agnostic autonomous selection of complex network features. This innovation allows the model to achieve enhanced accuracy performance while simultaneously reducing computational costs. We validated our approach through simulation experiments. The results demonstrated the method’s effectiveness in fusing and selecting multisource heterogeneous information within collaborative networks. It also excelled in identifying potential relationships between feature information and positional data, showcasing the robustness and applicability of our proposed solution in challenging collaborative target tracking environments.