近年来，大数据环境下的文本数据具有数据量大、分布广泛、动态增长、实时性要求高、噪声多等特点，成为近期文本挖掘并行处理技术的热点研究对象。然而，大数据环境下的文本挖掘并行处理技术具有性能不高、实时性得不到满足、挖掘准确性低等问题。因此，如何在大数据环境下设计高效的并行处理方案来提高文本挖掘效率与准确性是一个非常重要的问题。本课题着眼于“提高效率与准确性”这一关键问题，分析现有大数据环境下文本挖掘并行处理技术存在的问题，首先构建异构CPU-GPU集群大数据处理平台，利用其CPU-GPU协作计算能力，结合考虑负载均衡和数据通信问题，确立更具一般性的算法并行与硬件协同的策略；其次根据应用实时性等需求，展开基于特征工程同步并行生成的算法研究，以达到进一步提高文本挖掘性能的目的。最后，在保证准确性的前提下，设计基于深度学习噪声过滤的并行算法。课题的研究为未来文本挖掘并行处理技术提供重要的借鉴。

In recent years, text data in a big data environment has the characteristics of large amount of data, wide distribution, dynamic growth, high real-time requirements, and high noise, and has become a hot research object of recent text mining parallel processing techniques. However, text mining parallel processing techniques in a big data environment has the problems of low performance in parallel performance, lack of real-time performance, and low accuracy. Therefore, how to design an efficient parallel processing scheme in a big data environment to improve text mining efficiency and accuracy is a very important issue. This proposal focuses on the key problem of "improving efficiency and accuracy", and analyzes the existing problems of parallel processing techniques of text mining in existing big data environments. In this proposal, firstly, we construct a big data processing platform based on a heterogeneous CPU-GPU cluster and use its CPU and GPU cooperative computing capability, consider load balancing and data communication issues, establish a more general strategy of algorithmic parallelism and hardware collaboration. Secondly, according to requirements such as real-time applications, the research on parallel generation algorithm based on feature engineering synchronization is performed to further achieve the purpose of improving text mining performance. Finally, on the premise of ensuring the accuracy of mining, a parallel algorithm based on deep learning noise filtering is designed. The research of the subject will provide an important reference for the future text mining parallel processing technology.