This paper proposes an innovative approach to agricultural data visualization and economic benefit prediction based on ensemble learning, aiming to address the challenges of low prediction accuracy and insufficient visualization in agricultural economic forecasting. The AgriVis system integrates adaptive filtering and multi-level interactive analysis techniques, enabling it to process high-dimensional, spatio-temporally complex, and heterogeneous agricultural data. It achieves intuitive visualization of crop yields, environmental factors, and market dynamics, while supporting real-time data collection and intelligent analysis by smart agricultural devices such as autonomous tractors and drones. In addition, the KEAB (KNN-Enhanced Adaptive Boosting) algorithm combines KNN noise detection with AdaBoost ensemble learning, leveraging adaptive weight adjustment and a technical factor (TF) for optimization. This enhances prediction accuracy and robustness in small-sample scenarios, outperforming data-intensive deep learning models and traditional regression models. Experimental results show that, compared with traditional methods, the system achieves higher fitting accuracy and lower error in single-acre yield prediction. The integration of the system and the algorithm provides agricultural decision-makers with a closed-loop support system for “data visualization exploration, noise-resistant prediction, and planting structure optimization.” By clarifying the complex impacts of yield, cost, and technology on economic benefits, it helps decision-makers achieve precision agriculture and sustainable development.