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Concerning the problem of file format, low performance efficiency and image distortion in the application of e-books, a multiple-level optimization framework of cloud e-book is proposed, in which the optimization framework is mainly reflected in the following three aspects. Firstly, the performance of the vector graphics library is described, and an optimization algorithm is proposed to reduce the time complexity of the class library. Secondly, parallel coordinate system is used to calculate on an embedded GPU. Cloud electronic books in the vector graphics library has obtained a significant performance improvement by making use of the advantage of GPU in parallel computing. Thirdly, file conversion function of cloud e-books is passed on to the Hadoop cloud platform, by which cloud save energy consumption and computation time of the mobile equipment. And in order to optimize the Hadoop scheduler in the process of data location problem, location aware scheduler is applied to the proposed system. The experimental results show that, compared with the original VG Open class library, the performance of the cloud e-book system is improved by about 70%, and the computing time is reduced by about 60% compared with the continuous server platform.