In weak-texture environments, the current monocular visual-inertial Simultaneous Localization and Mapping (SLAM) suffers from visual degradation and error drift, leading to decreased accuracy in pose estimation. To address this issue, a monocular visual-inertial SLAM method is proposed based on feature collaboration. Initially, the Inertial Measurement Unit (IMU) data is pre-integrated, and a loosely coupled initialization with visual information is performed to obtain prior information and scale information of the system. Subsequently, a line feature extraction algorithm is introduced to optimize extracted line features, therefore reducing computational overhead. Based on positional relationships and geometric characteristics of point and line features, a feature collaborative association algorithm is employed to establish stable association constraints between point and line features, thereby enhancing the reliability of point feature tracking. Finally, a joint cost function optimization method based on multi-source information fusion is introduced to optimize point feature reprojection errors, line feature reprojection errors, and IMU residuals, resulting in improved pose estimation accuracy. Experimental results on the EuRoc and TUM Ⅵ public datasets, as well as in real environments, demonstrate that compared to mainstream visual-inertial SLAM methods, the proposed method reduces the average time consumption of line feature detection and tracking by 26.5%. Additionally, the root mean square error of pose estimation is reduced by an average of 38.6% and 43%. These findings validate that the proposed method achieves superior pose estimation accuracy in weak-texture environments.