In recent years, with the rapid development of computer information
technology, the development of artificial intelligence has been accelerating.
The traditional geometry recognition technology is relatively backward and the
recognition rate is low. In the face of massive information database, the
traditional algorithm model inevitably has the problems of low recognition
accuracy and poor performance. Deep learning theory has gradually become a very
important part of machine learning. The implementation of convolutional neural
network (CNN) reduces the difficulty of graphics generation algorithm. In this
paper, using the advantages of lenet-5 architecture sharing weights and feature
extraction and classification, the proposed geometric pattern recognition
algorithm model is faster in the training data set. By constructing the shared
feature parameters of the algorithm model, the cross-entropy loss function is
used in the recognition process to improve the generalization of the model and
improve the average recognition accuracy of the test data set.

利用 lenet-5 体系结构的权重共享和特征提取和分类的优势，构建了一个几何图形识别算法模型，通过在识别过程中使用交叉熵损失函数来提高模型的泛化性能和测试数据集的平均识别准确率。