Extensive research on automatic fake news detection has been conducted due to the significant detrimental effects of fake news proliferation. Most existing approaches rely on a single source of evidence, such as comments or relevant news, to derive explanatory evidence for decision-making, demonstrating exceptional performance. However, their single evidence source suffers from two critical drawbacks: (i) noise abundance, and (ii) resilience deficiency. Inspired by the natural process of fake news identification, we propose an Evidence-aware Multi-source Information Fusion (EMIF) network that jointly leverages user comments and relevant news to make precise decision and excavate reliable evidence. To accomplish this, we initially construct a co-attention network to capture general semantic conflicts between comments and original news. Meanwhile, a divergence selection module is employed to identify the top-K relevant news articles with content that deviates the most from the original news, which ensures the acquisition of multiple evidence with higher objectivity. Finally, we utilize an inconsistency loss function within the evidence fusion layer to strengthen the consistency of two types of evidence, both negating the authenticity of the same news. Extensive experiments and ablation studies on real-world dataset FibVID show the effectiveness of our proposed model. Notably, EMIF shows remarkable robustness even in scenarios where a particular source of information is inadequate.