In our previous work, a big data active learning algorithm based on MapReduce was proposed. In this paper, we transplant this algorithm into the Spark environment and propose a Spark based big data active learning algorithm. Furthermore, the two algorithms are experimentally compared on four aspects: running time, number of files, number of synchronizations, and memory cost. Some valuable conclusions are obtained,which can be very helpful to researchers in the related fields.