To address the issue of difficulty in accurately diagnosing children's pneumonia images, a classification and recognition method based on improved Inception-ResNet-v2 is proposed to improve the recognition accuracy of various types of children's pneumonia images. A multiscale channel attention module based on Inception-ResNet-v2 is introduced to promote network recognition and detection of targets under extreme scale changes. The size of the network stem layer's convolution kernel and the effective receptive field are increased at the start of the model. To avoid overfitting the model, the activation function is reduced in size, and the SiLU activation function is used instead of the ReLU activation function. To address the issue of less amount of data in the Chest X-ray dataset, the input image is rotated at a specific angle and flipped horizontally at random to improve the original data. The experimental results show that the proposed method has a 97.9% accuracy in the second classification of children's pneumonia data and an 85.8% accuracy in the third classification, demonstrating that the method can effectively improve the recognition accuracy of children's pneumonia.