The application of compression sensing technology was put forward based on K-SVD dictionary learning algorithm in forest microenvironment monitoring station. It would greatly reduce the number of data transmission, thus reducing the use of monitoring stations and prolonging the service life of monitoring stations. The air temperature data was used as the experimental object, and it verified the feasibility of the algorithm, and compared with the previous proposed compression sensing technique based on the discrete Fourier transform base. The experimental results were as follows: when the sparsity was the same, the average sparsity error of the learning dictionary based on K-SVD algorithm training was always less than that of the DFT dictionary when the original signal was sparsely represented, and the error distribution range was concentrated and basically lower than the median line of the DFT dictionary. When sparsity and compression ratio were the same, the average reconstruction error of learning dictionary was always smaller than that of DFT dictionary, and the error distribution range was more concentrated. To sum up, in the forest micro-environment monitoring station, the dictionary trained by the K-SVD algorithm had better sparse representation performance and reconstruction performance, which can reduce the operation power of the monitoring station while reducing the error of data transmission.