Data quality evaluation is one of the most critical steps during the data mining processes. Data with poor quality often leads to poor performance in data mining, low efficiency in data analysis, wrong decision which bring great economic loss to users and organizations further. Although many researches have been carried out from various aspects of the extracting, transforming, and loading processes in data mining, most researches pay more attention to analysis automation than to data quality evaluation. To address the data quality evaluation issues, we propose an approach to combine human beings' powerful cognitive abilities in data quality evaluation with the high efficiency ability of computer, and develop a visual analysis method for data quality evaluation based on visual morphology.