text: import matplotlib.pyplot as plt
  import numpy as np
  from scipy import interpolate
  
  # 设置距离
  x = np.array([0, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 70, 8, 9, 10])
  
  # 设置相似度
  y = np.array([0.8579087793827057, 0.8079087793827057, 0.7679087793827057, 0.679087793827057,
                0.5579087793827057, 0.4579087793827057, 0.3079087793827057, 0.3009087793827057,
                0.2579087793827057, 0.2009087793827057, 0.1999087793827057, 0.1579087793827057,
                0.0099087793827057, 0.0079087793827057, 0.0069087793827057, 0.0019087793827057,
                0.0000087793827057])
  
  # 插值法之后的x轴值，表示从0到10间距为0.5的200个数
  xnew = np.arange(0, 10, 0.1)
  
  # 实现函数
  func = interpolate.interp1d(x, y, kind='cubic')
  
  # 利用xnew和func函数生成ynew,xnew数量等于ynew数量
  ynew = func(xnew)
  
  # 原始折线
  plt.plot(x, y, "r", linewidth=1)
  
  # 平滑处理后曲线
  plt.plot(xnew, ynew)
  # 设置x,y轴代表意思
  plt.xlabel("The distance between POI  and user(km)")
  plt.ylabel("probability")
  # 设置标题
  plt.title("The content similarity of different distance")
  # 设置x,y轴的坐标范围
  plt.xlim(0, 10, 8)
  plt.ylim(0, 1)
  
  plt.show()