Spy technology in Positive and Unlabeled (PU) learning is easily susceptible to noise and outliers, which leads to the impurity of reliable positive instances, and the mechanism of selecting spy instances in the initial positive instances randomly tends to cause inefficiency in dividing reliable negative instances. To solve these problems, a PU learning framework combining new spy technology and semi-supervised self-training was proposed. Firstly, the initial labeled instances were clustered and the instances closer to the cluster center were selected to replace the spy instances. These instances were able to map the distribution structure of unlabeled instances effectively, so as to better assist to the selection of reliable negative instances. Then, the reliable positive instances divided by spy technology were purified by self-training, and the reliable negative instances which were divided as positive instances mistakenly were corrected by secondary training. The proposed framework can solve the problem of PU learning that the classification efficiency of traditional spy technology is susceptible to data distribution and random spy instances. The experiments on nine standard data sets show that the average classification accuracy and F-measure of the proposed framework are higher than those of Basic PU-learning algorithm (Basic_PU), PU-learning algorithm based on spy technology (SPY), Self-Training PU learning algorithm based on Naive Bayes (NBST) and Iterative pruning based PU learning (Pruning) algorithm.