The rapid evolution of modern society has triggered a surge in the production of diverse waste in daily life. Effective implementation of waste classification through intelligent methods is essential for promoting green and sustainable development. Traditional waste classification techniques suffer from inefficiencies and limited accuracy. To address these challenges, this study proposed a waste image classification model based on DenseNet-121 by adding an attention module. To enhance the efficiency and accuracy of waste classification techniques, publicly available waste datasets, TrashNet and Garbage classification, were utilized for their comprehensive coverage and balanced distribution of waste categories. 80% of the dataset was allocated for training, and the remaining 20% for testing. Within the architecture of DenseNet-121, an enhanced attention module, series-parallel attention module (SPAM), was integrated, building upon convolutional block attention module (CBAM), resulting in a new network model called dense series-parallel attention neural network (DSPA-Net). DSPA-Net was trained and evaluated alongside other CNN models on TrashNet and Garbage classification. DSPA-Net demonstrated superior performance and achieved accuracies of 90.2% and 92.5% on TrashNet and Garbage classification, respectively, surpassing DenseNet-121 and alternative image classification algorithms. These findings underscore the potential for executing efficient and accurate intelligent waste classification.