The paper presents a recommender algorithm for visual analysis based on Data field Schema and Aggregation, and developed an automated data analysis solution recommendation system (AutoEDA) in conjunction with the Exploratory Data Analysis process (EDA). To address the issue of field type recognition, we designed a dynamic field recognition framework combining four classification models to automatically classify data field types. For the evaluation of analysis strategies, we proposed an analysis strategy evaluation method based on the Chart Attribute Length for designing custom search strategies and defining chart matching rules. Finally, through interactive visual backtracking and the functionality of modifying and tracing analysis solutions, we collaborated with data analysts of varying experience levels, and validated the effectiveness and feasibility of the proposed solution with structured data from publicly available websites.