The development of radar technology has always been a hot issue. Especially in recent years, because of the popularization of Advanced Driver Assistance Systems (ADAS). This paper will focus on the angle detection part. We use the MUSIC (MUltiple SIgnal Classification) algorithm for high resolution requirement. In MUSIC algorithm, EVD (Eigenvalue Decomposition) costs the most computation load. We use cyclic Jacobi method to implement EVD processor, which can achieve hardware simplification. We propose to use the neural network model training arctangent, sine and cosine function and implement those models in hardware, which are used for plane rotation in cyclic Jacobi algorithm. Besides, we use the Static Floating Point (SFP) arithmetic in our neural network model, which can operate on the most efficient bits. The neural network model for trigonometric function has lower latency than the CORDIC method. We implement a NN-based Cyclic Jacobi EVD processor in TSMC 90 nm CMOS technology with high-Vt standard cell library. The latency of the system is 1.25 us, the total gate counts are 104.401k, and the power consumption is 15.4 mW (@0.9V).