To address the result instability caused by subjective visual estimation dependence in traditional ceramic tile color difference detection methods， a method integrating texture and color features was proposed for quantitation and grading of chromatic aberration in ceramic tiles. A large-scale dataset named TILE-TCR （TILE Texture and Color Recognition）， containing both texture and color labels， was constructed to enhance the model’s ability to represent texture and color features. At the same time， a color difference grading dataset named TILE-CAG （TILE Chromatic Aberration Grade） was established to support the color difference classification task. Based on these datasets， the network structure of Fractal Encoding Network （FENet） was improved by introducing Spatial Pyramid Pooling （SPP） and SE （Squeeze-and-Excitation） modules， thereby extracting multi-task features and focusing on critical regions. Then， a clustering algorithm was employed to determine the thresholds for color difference grading adaptively， thereby implementing objective quantification of color difference grading. Experimental results show that the proposed improved method achieves an accuracy of 92.82% in the ceramic tile texture classification task， representing a 1.84 percentage point improvement compared to the FENet； in the color difference grading task， the accuracy， precision， recall and F1 score of the proposed method exceed 90%. Furthermore， a simulated production line was built for industrial deployment and real-time performance test of the model. On commonly used ceramic tiles， the average detection time of the proposed method is under 3 seconds， meeting the real-time requirements for online inspection of industrial conveyor belts.