Although multi-span question-answering tasks align more closely with the complex demands of the real world, existing models often struggle to effectively model the dependencies and overall semantic structure between multiple answer spans. Therefore, we propose a concise and effective method for modeling span interactions, which primarily includes: 1) a Span Representation Module that utilizes SpanBERT to enhance span information within tokens; and 2) a Span Interaction Module that leverages two contrastive learning tasks to reinforce answer spans’ interaction within token representation. On one hand, we use the [CLS] token as an intermediary variable to carry information of span interaction, and on the other hand, we employ prompt-based tasks to further strengthen the multi-span question-answering reasoning capabilities of encoder and the span aggregation ability of the CLS token. Experiments demonstrate that baselines, on MultiSpanQA, incorporating our strategy achieved an improvement in EM F1 ranging from 2.88 to 11.27, achieving state-of-the-art (SOTA) results at equivalent model scales.