Abstract: Carbonate rock reservoirs in the Ordovician of Tahe oilfield representing western reservoirs are characterized by ultra-deep, high temperature, high pressure and fracture-cavity system. The number of deep or ultra-deep wells is increasing obviously with the deep exploitation of geological reserves. Formation energy decreased dramatically but hard to compensate, which led to the issue of continuously deepening setting depth of pump. This paper presents the analytical model of sucker rod pump depth based on the neural network, which can obtain the weight sequence of the influencing factors and determine the key element. Application in Tahe oilfield indicates that the prediction results have a high accuracy and the model can guide the design of artificial lift technology, deepen the pump depth utmost to guarantee the normal production of the ultra-deep well with low energy.