A solution is proposed for human presence recognition in indoor scenarios using FMCW radar. There are often issues of target loss or interference with small moving objects in the environment when it comes to recognizing stationary individuals. Firstly, a Range Doppler Map (RDM) is constructed using the sampled data from FMCW radar, and the precise distance subset corresponding to the target's location range is calculated through cumulative projection. Then, a high-precision micro-Doppler time-frequency feature map is obtained based on the distance subset using Short-Time Fourier Transform (STFT), from which 10 meaningful feature vectors are extracted. Finally, experimental data combined with Support Vector Machines (SVM) is used to screen different combinations of feature vectors. The experimental results show that there exists an optimal combination of 5 feature vectors, achieving a recognition rate of 97.98% for overall recognition of stationary individuals, environmental disturbances, and walking individuals in indoor environments. This method not only improves the performance of the classification model but also enhances its interpretability.