The invention discloses a 
switching algorithm based on 
machine learning in a UDN, and the 
algorithm comprises the following steps of A when a 
mobile device needs to be switched to a new 
micro cell each time, firstly reporting a feature information report of the position where the 
mobile device is located to a 
macro base station, and discretizing the feature information by the 
macro base station; Brespectively training the discretized feature 
information data by adopting four learners of 
machine learning 
decision tree prediction, neural network prediction, SVM prediction and 
random forest prediction to obtain each training model; C making a decision on each prediction result by using a majority voting method to obtain a decision result; and D when the decision result is that the 
mobile device needs to be switched and a pre-switching condition is met, using the 
macro base station to send a pre-switching request to a pre-switched target 
micro cell, and using the target 
micro cell to start to prepare resources for the mobile device and implement switching. According to the present invention, the unnecessary switching is reduced, and the average time 
delay of the 
system in the ultra-dense network is reduced.