Accurate classification and recognition of pulmonary nodules are key process of lung cancer computer aided diagnosis (CAD) system. Meanwhile，there are still some scientific and technical challenges, including the difficulty of the feature representation and samples labeled, and the lack of accurate and effective recognition and classification algorithms. A multi classification algorithm is presented combining weakly supervised ECOC algorithm with pulmonary nodules features expression of shape. In order to improve the classification accuracy, we select a series of accurate shape feature description vectors by deliberating the shape features of pulmonary nodules. During the training phase, the coded matrix is constructed by a series of binary classifiers, which are generated by a small amount of labeled pulmonary nodules from experts. Finally, the Humming distance between the code of testing sample and each row of the coded matrix are calculated to determine the category of the testing sample. Experimental results show that the proposed method can obtain more accurate classification results.