In the burgeoning field of computer vision, image classification and recognition have emerged as pivotal areas of research within computer science. This paper introduces a novel approach by amalgamating the Gramian Angular Field (GAF) method, recursive map technique, and Short-Time Fourier Transform (STFT) to devise a joint coding method for null spectrum analysis aimed at extracting spatial features from images. Subsequently, a hybrid classifier combining Deep Convolutional Neural Network (DCNN) and Support Vector Machine (SVM) algorithms—termed 3DCNN-SVM—is constructed to classify and recognize these extracted features. Further, this study conducts a comparative analysis between the proposed feature extraction and classification method and traditional algorithms. The focus of this evaluation is on the domain of vehicle label image recognition, aiming to ascertain the efficacy of the newly developed algorithm. This comprehensive comparison underscores the enhancements in accuracy and efficiency provided by the integration of 3DCNN-SVM in recognizing complex image features, thereby contributing significantly to the advancements in computer vision technology. The results show that after 35 iterations of training, the accuracy of 3DCNN-SVM reaches 99.5% with a loss value of 0.0877. Compared with ShffleNetV2 0.5×, ShffleNetV2 0.5×+ECA, ShffleNetV2 0.5×+ECA +Mish, ShffleNetV2 0.5×+ECA + Mish+SEP are 9.46%, 8.83%, 6.60%, and 5.52% higher on the average accuracy rate of 18 kinds of vehicle label images recognition, respectively. The 3DCNN-SVM classifier model constructed based on machine learning-related theory in this paper realizes the optimization of the traditional classification model and makes the classification and recognition of images more accurate, which has important practical significance.