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Enterprise performance evaluation is an important means of enterprise management, which can diagnose the whole development status of enterprise. Data envelopment analysis (DEA) is one of the most frequently used evaluation methods and support vector machine (SVM) is a novel method of data mining, which can be used for prediction and regression. Based on DEA and SVM, the paper proposes a method for evaluating and predicting enterprise performance. First, DEA method is used to evaluate DEA efficiency of all the oil refining enterprises performance. Then the input/output data and results of some decision making units (DMUs) are selected as the learning examples to train the SVM network and the others are used as the test examples to test the network. If the SVM network is testified well, a synthetic evaluation formula can be given to predict the DEA efficiency of a new DMU. A real example testifies the efficiency, practicability and intellectual ability of this method.

Measures programming is an important way for improving the effect of measures of oil fields, which can extend the stable production age limit of oil fields, reduce the mining difficulty and improve oil-producing speed and final recovery ratio. Based on the theory of stochastic chance-constrained goal programming, a stochastic chance-constrained goal programming model is put forward for measures programming of oil fields, which considers the per increasing production and per increasing cost of measures as stochastic variables, uses the increasing production and increasing cost as objectives and the total work load and work load of each measure as constraints. At the same time, the process of its hybrid intelligent algorithm including stochastic simulation, genetic algorithm and neural network is also provided. Finally, the real example, i .e. measures programming of DX Oilfield testifies the efficiency, practicability and intellectual ability of the model and its solution algorithm, which provides a new decision basis for the incremental measures program of oil fields.