Concerning the safety and monitoring problems of the blind people during traveling, the design of a networked cane system for blind people based on machine learning algorithms was proposed. Multiple functions were added to the system, such as obstacle avoidance, positioning, alarm and communication. First, infrared obstacle avoidance and ultrasonic ranging obstacle avoidance were designed as the basic functions of the system, which could be used to detect road conditions and obstacles for the daily travel of the blind and provide real-time voice and motor vibration reminders. Second, remote communication function for help was added to the system, which was able to send help text messages and phone calls to specific mobile numbers. In addition, Global Positioning System (GPS) function, accelerometer gyroscope attitude angle calculation function and abnormal attitude alarm function based on K-Nearest Neighbor (KNN) and Dynamic Time Warping (DTW) algorithms were also added, which were able to transfer all kinds of information data to the cloud server storage. Finally, the WeChat mini program was used to replace the native APP as the monitoring operation interface, and functions such as one-click alarm, weather query, blind safety information were provided. Test results show that the proposed system has the attitude recognition success rate reached 86%, and has the accuracy improved by nearly 31% compared to the attitude angle system. The networked cane system for blind people can greatly improve the security of the blind during traveling, so that the blind can ask for help in time when an accident occurs, and achieve the safe monitoring and positioning monitoring of the blind postures.