Focusing on the detection problem of false data that conform to a certain pattern or rule, a false trend time series detection method based on randomness analysis was proposed. Based on the analysis of time series composition, firstly the simple forgery method and complex forgery method of false trend time series were explored, and decomposed into two parts:false trendness and false randomness. Then the false trend of time series was extracted by the approximation of base function, the false random of time series was analyzed with the randomness theory. Finally, monobit frequency and frequency within a block were adopted to test whether the false random part has randomness, which established a detection method of false time series with a certain trend feature. The simulation results show that proposed method can decompose the false time series and extract the false trend part effectively, meanwhile realize the detection of simple and complex forged data. It also supports the authenticity analysis for the numerical data obtained by means of observation or monitoring equipment, which improves the discrimination range of false data with average detection accuracy of 74.7%.