Capturing feature information effectively is of great importance in the field of computer vision. With the development of convolutional neural networks, concepts like residual connection and multiple scales promote continual performance gains in diverse deep learning vision tasks. In this paper, novel residual feature-reutilization inception and split-residual feature-reutilization inception are proposed to improve performance on various vision tasks. It consists of four parallel branches, each with convolutional kernels of different sizes. These branches are interconnected by hierarchically organized channels, similar to residual connections, facilitating information exchange and rich dimensional variations at different levels. This structure enables the acquisition of features with varying granularity and effectively broadens the span of the receptive field in each network layer. Moreover, according to the network structure designed above, split-residual feature-reutilization inceptions can adjust the split ratio of the input information, thereby reducing the number of parameters and guaranteeing the model performance. Specifically, in image classification experiments based on popular vision datasets, such as CIFAR10 (97.94%), CIFAR100 (85.91%), Tiny Imagenet (70.54%) and ImageNet (80.83%), we obtain state-of-the-art results compared with other modern models under the premise that the models’ sizes are approximate and no additional data is used.