Existing rock mass stability analysis methods are mostly based on simplified structural models, making it difficult to accur ately reflect the complex spatial network characteristics of structural planes. To address the lack of intelligent construction methods for three-dimensional structural plane networks and stability analysis using UAV-acquired data, this paper is based on UAV oblique photogrammetry to obtain multi-view images of rock masses. A deep learning model is constructed by fusing geometric and texture features to achieve automatic identification of rock mass structural planes and intelligent extraction of attitude parameters, thereby constructing a three-dimensional structural plane network model. This network is introduced into rock mechanics analysis and combined with block theory to automatically identify potentially unstable blocks and quantitatively calculate their stability. Finally, method validation is carried out from four dimensions: structural plane identification accuracy, block identification integrity, network topology rationality, and stability analysis consistency, while 3D visualization is used to reveal rock mass failure modes and stability characteristics. Experimental results show that the proposed method achieves an accuracy of 94.2%, a recall rate of 92.5%, and an intersection over union (IoU) of 87.6% for structural plane recognition. Block identification results indicate that when the volume increases from 10 m³ to 1000 m³, the safety factor decreases from 1.3 to 0.6, revealing the scaling effect law. By fusing geometric and texture features, this method optimizes the structural plane identification model, enhances the topological integrity of three-dimensional network construction, and elevates the dimension of rock mass stability analysis from individual structural planes to the network level, significantly improving the accuracy and engineering applicability of stability assessment.