In the Internet of Things, the state recognition of laser communication equipment is crucial for the accuracy of data transmission and scheduling. Currently, the device status mainly relies on the controllers and sensors in the DCS equipment, which are determined by setting a single threshold. But as the complexity of the equipment increases, the accuracy of this method is affected. To this end, a machine learning based method for laser communication equipment state classification and recognition was studied. Using time series sliding window mode to partition the state feature vectors of laser communication equipment; Define the abnormal status level of laser communication equipment based on its characteristic attributes that have an alarm effect; Building a laser communication equipment state recognition model based on machine learning fusion of alarm features. The experimental results show that by using different types of laser communication equipment as test objects and setting their fault states in different scenarios, the research method can achieve state recognition of test equipment in various scenarios, which has practical value.