In　view　of　the　self-balancing　movement　control　problem　of　the　two-wheeled　robot,　a　bionic　self-learning　algorithm　of　the　robot　is　proposed　as　a　study　mechanism　of　Skinner＇s　operant　conditioning　reflection　based　on　the　Boltzamnn　machine.　This　algorithm　uses　the　Metropolis　criterion　in　Boltzamnn　machine　to　balance　in　the　proportion　of　the　exploration　and　the　exploitation　in　the　study　of　Skinner＇s　operant　conditioning　reflection,　and　chooses　the　most　superior　behavior　through　certain　probability　depending　on　the　probability　tropism　mechanism.　Thus　the　robot　can　obtain　the　skill　of　bionic　self-learning　like　the　human　or　the　animal　under　the　unknown　environment,　and　realize　the　self-balancing　movement　control　of　the　robot.　Finally,　the　simulation　experiments　were　conducted　and　the　Skinner＇s　operant　conditioning　reflection　study　algorithms　based　on　the　Boltzamnn　machine　and　the　greedy　strategy　were　compared,　separately.　Results　show　that　the　Skinner＇s　operant　conditioning　reflection　study　algorithm　based　on　the　Boltzamnn　machine　can　obtain　the　stronger　movement　balancing　control　skill　and　the　better　dynamic　performance,　and　manifest　the　self-learning　characteristics　of　the　robot.