The invention puts forward a Spark-based big data hybrid model mobile recommending method comprising four steps as follows: firstly, getting a user's commodity purchase data at a mobile end; secondly, extracting user historical data from a database and importing the user historical data to an HDFS, and extracting features, such as user behavior features, brand features, user's personal consumption features and cross features; thirdly, packaging a hybrid model on a Spark platform using an RDD operator, and embedding the model interface into a big data platform for calling; and fourthly, calling the hybrid model interface to extract feature data, setting training parameters of the model, and training the hybrid model. The model is estimated using a test data set and optimized, the trained hybrid model is saved, and relevant recommendation is made. The method can effectively improve the efficiency of recommendation under the condition of a larger amount of data and higher data sparseness.