In order to alleviate the difficulties in collecting indexes for the analysis of farmland weed communities, we implemented a computer vision technology-based method for the identification of farmland weeds at the species level. By using the super-green and maximum interclass difference methods to obtain a green vegetation binary image, we were able to separate weeds from cultivated crops through multiple etching and the removal of small areas. A BP (back propagation) neural network was used for weed recognition, and the morphological characteristics of the weeds and each region were selected following etching to construct the input matrix of the recognition model for training and testing the BP network. After experimenting with the computational vision method for the identification of five weed species, we discovered that the recognition accuracy rate reached 96%. The results showed that the computer vision method could quickly and accurately extract a weed community analysis index, thereby providing a reference for the intelligent analysis of weed communities.