Condition random fields has been successfully applied to various applications in text analysis, such as sequence labeling, Chinese words segmentation, named entity recognition, and relation extraction in nature language processing. The traditional CRFs tools in single-node computer meet many challenges when dealing with large-scale texts. For one thing, the personal computer experiences the performance bottleneck; For another, the server fails to tackle the analysis efficiently. And upgrading hardware of the server to promote the capability of computing is not always feasible due to the cost constrains. To tackle these problems, in light of the idea of “divide and conquer”, we design and implement SparkCRF, which is a kind of distributed CRFs running on cluster environment based on Apache Spark. We perform three experiments using NLPCC2015 and the 2nd International Chinese Word Segmentation Bakeoff datasets, to evaluate SparkCRF from the aspects of performance, scalability and accuracy. Results show that: 1)compared with CRF++, SparkCRF runs almost 4 times faster on our cluster in sequence labeling task; 2)it has good scalability by adjusting the number of working cores; 3)furthermore, SparkCRF has comparable accuracy to the state-of-the-art CRF tools, such as CRF++ in the task of text analysis.