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<sub>Fusion integration method has been widely used in the field of pattern recognition. However, some base classifiers have poor real-time performance stability, </sub><sub>which c</sub><sub>auses poor performance of multiple classifiers. A new multi-classifier-based sub-fusion integration classification is proposed for the above problems. </sub><sub>T</sub><sub>his method considers the dynamic selection of various classifiers at the level of measurement layer fusion, </sub><sub>t</sub><sub>he category with the highest number of votes is the category identified by the feature vector in the fusion system</sub><sub> to constitute a new adaptive sub-fusion integration classifier method</sub><sub>. Experiments show that this method is significantly more accurate than conventional classifiers and classification fusion methods and has better robustness.</sub>