Abstract: Addressing the issues of lacking support for field sequence focusing, high noise in chart recommendation candidates, and the disconnect between data segmentation and visual presentation in automated explorato-ry analysis of large-scale complex data, an automated exploratory data analysis method that integrates iter-ative data segmentation and visualization is proposed. Firstly, a field evaluation mechanism based on at-tribute frequency statistical feature ratios is introduced to quantify the richness and distribution concentra-tion of attribute values, achieving efficient focusing on key field sequences. Then, a data filter based on parallel coordinates is adopted to support query optimization oriented towards user reasoning and cogni-tion, enhancing the effectiveness of iterative exploration of data subsets. Furthermore, a visual analysis recommendation algorithm based on a field type search tree is constructed by combining chart encoding length, data fields, and aggregation method paradigms, generating interpretable charts that match the cur-rent data constraints. Finally, an interactive visual backtracking mechanism is incorporated to implement the exploration function of cluster comparison portraits, constructing a closed-loop process where data segmentation and visualization are seamlessly integrated. Based on this, a visual recommendation system has been developed and implemented. Experimental results on public structured datasets such as urban air pollution, physical examination, and medical insurance, compared with methods like AutoProfiler and AdaVis, show that the proposed method significantly improves the recommendation validity (Valid@10) under a Top-10 budget, with an interactive response delay stabilized at approximately 2 seconds and a high-quality candidate template diversity maintained at around 6.0. Additionally, in user evaluations by professionals from multiple backgrounds, the mean subjective score for automated data exploration man-agement and visual analysis recommendation dimensions exceeds 4 points, verifying the feasibility and exploration flexibility of the proposed method.