Deep Neural Network （DNN） may encounter samples that are not present during training， and there are difficulties in rejecting unknown class samples accurately. Open set recognition can classify known class samples accurately while rejecting unknown class samples. In the current domain of open set recognition， prototype learning methods have gained widespread applications. However， these methods cannot ensure intra-class compactness and inter-class separability of sample distribution simultaneously. Therefore， an open set recognition method with Open Generation and Feature Optimization （OGFO） was proposed. Firstly， the concept of open point was presented， and the inherent features of corresponding class samples were learned by prototype points through DNN. The average of prototype points for each class was open point， representing the inherent features of unknown classes， and the central region of the feature space was occupied by the open point as open space for the distribution of unknown class samples. Then， a Feature Optimization Algorithm based on open points （FOA） was proposed， so that open points were utilized to force a more compact distribution within the same class samples， and a more separable distribution among different class samples. Finally， an open point based generation method OGAN （Open Generative Adversarial Network） was proposed， and the unknown class samples generated by OGAN were forced by DNN to distribute in the open space occupied by open points. Experimental results demonstrate that compared with Adversarial Reciprocal Points Learning for open set recognition （ARPL） on datasets such as MNIST， SVHN， CIFAR10， and TinyImageNet， OGFO has significant improvements in AUROC （Area Under the Receiver Operating Characteristic curve）. Especially， on TinyImageNet dataset， OGFO improves the AUROC by at least 3 percentage points and has improvements of at least 6 and at least 5 percentage points， respectively， in accuracy and OSCR （Open Set Classification Rate） compared to ARPL. It can be seen that the challenge which other methods cannot address： balancing intra-distribution compactness and inter-distribution separability of sample distribution simultaneously is addressed by OGFO.