Floorplan design is an important step of house design. However， the existing automatic floorplan design methods lack the common constraints of considering user requirements and building boundaries. Thus， these methods suffer from unreasonable layout problems such as missing corners of generated room， severe occlusion between rooms and room getting out of the boundary. In order to solve the above problems， a building floorplan GBC-GAN （Graph Boundary Constrained-Generative Adversarial Network） was proposed based on user requirements and boundary constraints， and the proposed method consists of a constraint layout generator and a room relation discriminator. Firstly， the user-specified floorplan layout requirements （including the number and types of rooms and the adjacency relationship between houses） were transformed into a constraint relation graph structure， after that， the building boundary and constraint relation graph were encoded separately for feature fusion. Then， by adding the prediction module of bounding box， the constraint layout generator was used to convert the floorplan generation problem into a bounding box generation problem of each room object， and the geometric boundary optimization loss was used to solve the problems of severe occlusion between rooms and room getting out of the boundary. Finally， the room bounding box layout and the constraint relation graph were input into the room relation discriminator for training to generate the floorplan layout meeting the room objects and their relations. The Frechet Inception Distance （FID） and Structural Similarity Index Measure （SSIM） of the proposed method are improved by 4.39% and 2.3% compared with those of House-GAN on the large-scale real building dataset RPLAN. Experimental results show the proposed method improves the rationality and authenticity of the floorplan layout under different user requirements and boundary constraints.