In the realm of low-consumption computing technologies for big data analytics, target tracking often involves processing massive and continuous video streams. The discriminative correlation filter is a resource-efficient technique that learns filters through regression and generates response maps by convolving filters with feature representations, thus pinpointing targets accurately. However, most tracking models predominantly optimize temporal and spatial information, overlooking variations in the relative importance and correlations across feature channels. To address these issues within an energy-conscious computing framework, we introduce a correlation-based attention learning model. First, we calculate a similarity matrix for feature channels and use $K$-nearest neighbor to identify each channel’s neighborhood, allowing the filter to harness shared information among similar channels. Then, we construct a $p$-Laplacian matrix from the similarity data to guide the update of channel weights, capturing comprehensive feature relationships while enabling flexible adjustment of the order $p$ for different datasets. By doing so, our model more accurately redistributes each channel’s contribution, improving adaptability under diverse conditions while aligning with the efficiency constraints of big data analytics. Experiments on prominent public datasets confirm that our method not only delivers higher discriminative power and superior tracking performance compared to existing techniques but also upholds the principles of low-consumption computing, making it particularly suitable for large-scale, resource-aware scenarios.