Chromosome karyotype analysis is of great significance in prenatal screening and genetic disease diagnosis. However， existing chromosome classification models are generally limited by insufficient feature extraction capability， high sensitivity to image quality， and inadequate attention to local details， leading to low overall classification accuracy， particularly the frequent misidentification of short chromosomes. Therefore， a coarse-to-fine chromosome cascaded classification framework integrating image texture enhancement and super-resolution techniques was proposed. Firstly， chromosomes were coarsely classified based on the International System for human Cytogenetic Nomenclature （ISCN）， and they were divided into long chromosomes and short chromosomes groups to mitigate class imbalance and feature confusion. Secondly， for the long chromosome classification task， a feature enhancement module was added to optimize the classification model's ability to perceive details of long chromosomes. Thirdly， considering the characteristics of short chromosome images， the super-resolution technique was introduced to improve image quality and the model's perceptual capability. Experimental results on a private dataset showed that the proposed framework achieved an overall chromosome classification accuracy of 98.91% and an overall chromosome F1-score of 98.77%， with 99.01% for long chromosomes and 98.31% for short chromosomes. By adopting differentiated classification strategies and task-specific models， this cascaded chromosome classification framework significantly enhances both classification accuracy and model robustness.