Abstract: Abstract: The damage of weeds in the field to the seedlings of crops can seriously affect the photosynthesis of plant seedlings. To solve this problem, and to prevent weeds from affecting the growth of crop seedlings, it is of great significance to accurately identify crop seedlings and weeds. This paper proposed a model to identify weeds based on improved convolutional neural network (CNN). Aiming at the problems of many parameters of traditional Alex Net model and single feature scale, this paper adjusted the network structure of the traditional model by combining dilated convolution and global pooling, and extended the single convolution kernel into multi-scale convolution kernel then fused. It can optimize the training time and achieve high precision. We regarded each convolution layer input to calculate the bulk mean and variance, and then used the batch normalization while reducing some of the characteristics of the layer figure at the same time. We also used the global pooling to take the place of the last full connection layer. The model consists of 7 convolution layers, 1 fusion layer and 4 pooling layers. In image preprocessing phase, in order to prevent the deviation of the trained model caused by the unbalanced distribution of sample numbers, we had zoomed, flipped and rotated the original pictures of dataset randomly to get the augmented dataset, and used 80% of the pictures as the train dataset and the rest as the test dataset. These pictures were quantized to 256×256 dpi for CNN (convolutional neural network) training, and the original dataset and augmented dataset were used to train models. In order to find the optimal expansion coefficient, the expanded convolution kernels with expansion factor of 2 and 4 were used respectively at the first layer of convolution kernel. In addition, we compared the global average pooling with the global maximum pooling. The results showed that the global average pooling is better, which means that the average pooling can reduce the variance of the estimated value due to the limited size of the neighborhood; the background information of the image was retained more, it was more helpful for extracting the key features. However, the maximum pooling preserves more texture information and easily leads to the loss of the extracted deep feature information. According to various kinds of parameters, 8 sorts of models with different expansion coefficients and pooling types were designed. In order to further optimize the model and improve the average recognition accuracy, we also compared different batch sizes such as 64, 128, and 256, respectively. It can be seen that increasing batch size can increase memory utilization within a reasonable range and the efficiency of parallelization of matrix multiplication is improved. The number of iterations required for a single epoch (full dataset) is reduced, the processing speed for the same amount of data was accelerated, and then the optimal model was obtained. For the performance evaluation of the model, we use the average recognition accuracy and confusion matrix visualization to judge. Compared with the traditional Alex Net model, the optimal model can reach the recognition accuracy of more than 90% only after 4 training epochs, the memory requirement was far lower than the traditional one, and the average test recognition accuracy can reach 98.80%, which is due to that the improved model adopted the wider network structure and the method for preparation of global pooling. This may increase the multi-scale fusion of feature maps, enhance the relationship between figure and categories and keep the invariance of the input space transformation, so the recognition ability of different crop seedlings and weeds is stronger. It can be seen from the confusion matrix that the classification performance of the optimal model is high, which can be used as a reference for the development of intelligent identification device of weeds and seedlings.