Infrared small target detection and tracking technology plays a essential role in systems like infrared warning, guidance, and missile defense. Efficiently and reliably detecting fast-moving, small targets is a key technological challenge. This study introduces a dense convolutional attention network as a potential solution to address the identified challenge. This network incorporates a dense convolutional interaction module that effectively minimizes information loss across different levels. Additionally, a simple, parameter-free attention module is introduced to adaptively extract features and enhance the emphasis on important information. Furthermore, a dense pixel contrastive learning module is introduced to capture more features of targets with similar sizes by constructing positive and negative sample pairs and designing a pixel contrastive loss function. Experimental validation was conducted on four representative datasets, demonstrating the success of our approach in terms of performance superiority over other methods.